Abstract
Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework of this study is established. Secondly, the technical framework is constructed based on the supervised learning model. Finally, the comprehensive performance of the model is evaluated using a dataset and practical application. The results show that when the model is used to process public sports service data, its performance is excellent. Specifically, the model’s accuracy and recall in processing various types of data markedly exceed expectations, with the accuracy reaching more than 88% and the recall remaining at a similarly high level. This remarkable result not only validates the supervised learning model’s practicability in the quality optimization of public sports services but also highlights its huge application potential and value. In addition, the possibility and challenge of the model in practical application are also discussed, which provides a useful reference for further improving the quality of public sports service. The findings of this study enrich the research methods in the field of public sports services and offer a scientific basis for relevant decision-making, which helps promote the continuous optimization and development of public sports services.
Introduction
Research background and motivations
As a vital part of the development of modern society, public sports service is not only related to the improvement of national physique but also an important embodiment of the prosperity and development of cultural and sports undertakings1. However, with the growing public demand for sports services, how to improve the quality of public sports services to meet diversified and personalized needs has become an urgent problem to be solved2. At the same time, the swift progress of information technology (IT), especially the extensive application of big data, artificial intelligence, and other technologies, has provided new ideas and methods for the optimization of public sports services3.
In this context, this study is based on the supervised learning model to optimize the quality of public sports services. Through the construction of an appropriate supervised learning model, the data in the process of public sports service is collected, analyzed, and processed, and the key factors affecting the quality of service are explored. Then, the targeted optimization measures are proposed. This can help to improve the efficiency and effect of public sports services, offer a scientific basis for relevant policy formulation, and promote the healthy development of public sports undertakings.
Moreover, this study also has critical theoretical significance and practical value. At the theoretical level, the application research of the supervised learning model in the domain of public sports service can enrich and improve the relevant theoretical system and provide theoretical support for subsequent research. At the practical level, the outcomes of this study can be directly applied to the actual work of public sports services, and furnish specific guidance for enhancing service quality and meeting public needs, which has important practical significance and application prospects.
Research objectives
The purpose of this study is to deeply explore the optimization path of public sports service quality and its social impact. Specifically, the objectives of this study are as follows:
(1)
The key factors of public sports service quality are accurately identified. Through systematic analysis of multi-dimensional data such as service process, facility conditions, and personnel allocation, a comprehensive and detailed service quality evaluation framework is constructed, laying a solid foundation for subsequent model construction and optimization.
(2)
A public sports service quality evaluation system based on the supervised learning model is constructed. The supervised learning algorithm is used to train and learn a large amount of historical data, improve the accuracy and stability of the model’s prediction of service quality, and realize the objective and quantitative evaluation of service quality.
(3)
Targeted optimization strategies for public sports service quality are proposed. According to the actual situation, feasible optimization measures are formulated for the weak links in the service process.
(4)
The influence of optimized public sports service quality on public sports participation and sports health level has been deeply explored. By comparing and analyzing the data changes before and after optimization, the actual effect of optimization measures is evaluated, and the deep reasons and mechanisms behind it are discussed, to afford a scientific basis for policy formulation.
To sum up, this study comprehensively employs a supervised learning model, data analysis, and other professional technical means to synthetically and deeply explore the optimization of public sports service quality and its social influence, to contribute to the sustainable and healthy development of public sports.
Literature review
The study of the optimization of public sports service quality and its social impact has always been a hot spot in the interdisciplinary research of sports science, sociology, and management4. First, the research on the quality of public sports services has been deeply discussed by domestic and international scholars from different angles. The concept, influencing factors, and evaluation methods of service quality have become the core contents of the research5. Among them, facilities, service processes, personnel quality, and other aspects are considered to be the key factors affecting service quality6. Simultaneously, with the development of IT, the application of information services in sports services has gradually attracted attention7.
Schiappa et al. (2023) first defined the connotation of public sports service quality and constructed a comprehensive evaluation system from aspects such as facility conditions, service processes, and personnel quality. Through on-site investigations and data analysis of multiple public sports venues, the study found numerous issues in facility maintenance, service efficiency, and personnel training in current public sports services. Addressing these problems, the study proposed specific strategies such as enhancing facility maintenance, optimizing service processes, and improving personnel quality, aiming to enhance the public sports services’ overall quality8. Ramon et al. (2022) focused on the application of digital technology in innovation within public sports services. Through in-depth investigations and field interviews, they analyzed how digital technology reshaped the patterns and processes of public sports services. The research revealed that the application of digital technology not only enhanced the convenience and personalization of services but also promoted the optimization and sharing of sports resources9. Smith et al. (2023) utilized big data analysis and mining techniques to deeply process and analyze large amounts of data in the process of public sports services. By constructing a service quality evaluation model, they successfully identified critical factors influencing service quality and proposed targeted improvement measures. This study provided a scientific basis for the quality management of public sports services and laid a solid foundation for improving public satisfaction and sports health levels10.
Second, the supervised learning model’s application in service quality optimization is gradually becoming prominent. This model can predict future service demands, optimize resource allocation, and improve service quality by training and learning from historical data11. In the field of public sports, some studies have attempted to apply the supervised learning model to evaluate and predict service quality, achieving certain effectiveness12. However, current research is still in its early stages, and aspects such as model selection, data processing, and result interpretation require further improvement.
Bunker et al. (2021) utilized the supervised learning model to successfully predict future public sports service demand by analyzing historical service data. The model effectively identified the seasonality, periodicity, and sudden changes in service demand based on facility usage, personnel flow data, and user feedback. This provided a scientific basis for the planning of public sports facilities, personnel allocation, and adjustment of service content, contributing to the improvement of service efficiency and quality13. Pelati et al. (2022) applied supervised learning algorithms to construct a multidimensional service quality evaluation model. This model comprehensively considered aspects such as service attitude, facility conditions, and service processes. By training and learning from a large amount of user evaluation data, the model achieved an objective and accurate assessment of service quality. The research results indicated that the supervised learning model exhibited high accuracy and stability in service quality evaluation, offering strong support for the continuous improvement of service quality14. Tang et al. (2021), through a comparative analysis of the application effects of different supervised learning models in optimizing public sports services, proposed a data-driven service optimization strategy. This strategy involved real-time monitoring of service data, utilizing model predictions of changing service demand trends and adjusting service content and methods accordingly. Practical results demonstrated that this data-driven service optimization strategy based on supervised learning models notably enhanced service satisfaction and user retention, thus playing a crucial role in improving the public sports services’ overall competitiveness15. The existing studies are summarized in Table 1.
Table 1 Research status statistics.
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In summary, despite certain progress in the research on public sports service quality and its impact, there are still many shortcomings and unresolved issues. Building upon this foundation, this study further explores the application of supervised learning models in optimizing public sports service quality and deeply analyzes the specific effects of optimized service quality on public sports participation and sports health levels. By comprehensively employing various research methods and technical means, the aim is to provide scientific evidence and practical guidance for the public sports services’ continuous improvement and healthy development.
Research model
This study focuses on improving public sports service quality and its social impact. In the modern social environment, public sports service, as a critical channel to promote the national fitness program and improve national physical health, has a direct impact on the public’s participation in sports activities and overall physical health. However, the current public sports service has many deficiencies in terms of facilities, service processes, personnel quality, etc., resulting in uneven service quality, which makes it difficult to meet the growing demand of the public16,17,18,19,20. Therefore, optimizing public sports service quality and improving public sports participation and physical health levels has become an urgent problem to be solved in this study.
The theoretical framework of this study is mainly based on the theory of service quality, public sports service, and IT applications. Firstly, the service quality theory is the cornerstone of this study. This theory emphasizes that service providers should pay attention to customers’ needs and expectations, and satisfy customers’ satisfaction by providing high-quality services. In the field of public sports services, this means the need to pay attention to the needs and expectations of the public on sports facilities, service processes, personnel quality, and so on, and optimize the service quality accordingly. Secondly, the public sports service theory provides concrete theoretical support for this study. The theory emphasizes the public welfare and universality of public sports services, aiming to promote the development of national fitness by offering high-quality sports services. Based on the relevant viewpoints of public sports service theory, this study analyzes the current situation and influencing factors of public sports service quality and proposes targeted optimization strategies21,22,23. Finally, the IT application theory furnishes theoretical support for applying a supervised learning model in optimizing public sports service quality. With the continuous development of IT, machine learning techniques such as supervised learning models have been widely used in various fields. In the field of public sports services, the supervised learning model can be used to conduct training and learning from historical data, predict future service demand, optimize resource allocation, and improve service quality24,25,26,27,28.
This study employs multiple research methods to systematically explore how to improve public sports service quality. It mainly includes literature analysis, field research, questionnaire survey29,30,31, data analysis, and IT application theory32,33. The details are detailed below.
This study aims to identify key factors that affect public sports service quality through theoretical analysis and empirical research. Moreover, it proposes targeted optimization strategies to enhance public sports service quality, promote the implementation of national fitness programs, and improve the overall physical health level of the population. Through the comprehensive application of various research methods such as field research, literature analysis, questionnaire survey, data analysis, and supervised learning model application, this study can provide comprehensive theoretical support and empirical basis for improving public sports service quality34,35,36. The theoretical framework of the study is illustrated in Fig. 1.
Fig. 1
figure 1
The theoretical framework of this study.
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Figure 1demonstrates that the innovation of the theoretical framework lies in its comprehensiveness and foresight. It innovatively integrates public sports service theory, service quality theory, and IT application theory, constructing a multidimensional and interdisciplinary analytical framework37. This innovation not only breaks through the limitations of traditional research on single theories but also fully considers the driving role of modern IT applications in optimizing the quality of public sports services. Furthermore, this theoretical framework also emphasizes practical application, highlighting the close integration of theoretical guidance and empirical research, furnishing feasible ideas and strategies for improving the quality of public sports services38. Hence, the theoretical framework’s innovativeness plays an essential guiding role in promoting research and practice in public sports services.
Based on this, guided by the theoretical framework, this study innovatively applies supervised learning models to the optimization research of public sports service quality, achieving the construction of technical models39. As a powerful machine learning technology, the supervised learning model can accurately predict future service demands by mining potential patterns and trends in historical data through training and learning40. In the domain of public sports services, by utilizing supervised learning models to analyze and process massive service data, key factors affecting service quality can be identified, and resource allocation and service strategies can be optimized accordingly41,42,43. The results of the technical model construction based on supervised learning models are displayed in Fig. 2.
Fig. 2
figure 2
Technical model based on supervised learning.
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In Fig. 2, this study constructs a technical model within the research framework, enabling a more precise assessment of the public’s demand for and expectations of public sports services. Specifically, it allows for an in-depth analysis of the public’s detailed requirements in areas such as sports facility characteristics, service process optimization expectations, and staff competency demands. This, in turn, provides strong support for formulating personalized service plans that align with public needs, effectively enhancing overall public satisfaction and participation enthusiasm. At the same time, applying the technical model has strongly promoted the development of public sports services towards greater intelligence and automation, achieving a significant improvement in service efficiency and quality44,45,46,47.
The methods employed in this study are diverse and systematic, covering the following aspects:
(1)
Literature review: This study conducts a comprehensive and in-depth review of relevant domestic and international literature. The focus is on the research status, existing issues, and improvement strategies concerning the quality of public sports services. By systematically summarizing existing research findings, this study extracts valuable theoretical insights and practical guidance, laying a solid theoretical foundation and providing abundant practical reference examples.
(2)
Field research: The study involves detailed field investigations of various public sports facilities across different regions. It aims to thoroughly understand the usage, maintenance, and user feedback regarding these facilities. By collecting firsthand data, the study gains deep insights into the specific problems and challenges faced by public sports services in actual operations, offering detailed practical evidence for subsequent research.
(3)
Survey questionnaire: A multidimensional questionnaire covering aspects such as satisfaction with sports facilities, service process satisfaction, and staff quality satisfaction is designed and widely distributed to different groups. By gathering extensive user feedback and applying scientific statistical analysis methods, the study aims to gain a deep understanding of the public’s actual needs and expectations, providing reliable quantitative evidence for optimizing service quality.
(4)
Data analysis: This study utilizes various statistical methods. Descriptive statistics are first applied to display basic survey results and data distribution characteristics. Based on this, correlation and regression analysis are employed to investigate the extent to which different factors impact the quality of public sports services, accurately identifying key influencing factors and providing strong data support for formulating optimization strategies.
The application of IT is primarily reflected in the construction and application of supervised learning models. By extensively collecting and organizing large amounts of historical data, and applying supervised learning algorithms for deep training and learning, a model with strong predictive capabilities is developed. This model can achieve accurate predictions of future service demands, optimize resource allocation, formulate personalized service plans, and effectively enhance service quality. Specifically, based on users’ historical behavior and feedback data, the model can accurately identify user needs and expectations, enabling the customization of personalized services. Additionally, the application of the model drives the intelligent and automated transformation of public sports services, significantly improving service efficiency and quality. Through the organic combination and collaborative application of various research methods, this study systematically analyzes the current state and issues of public sports service quality and proposes practical optimization strategies. The ultimate goal is to comprehensively improve public sports service quality, vigorously promote the implementation of national fitness programs, and effectively enhance public participation in sports and overall health. Throughout the research process, literature review, field research, survey questionnaire, data analysis, and the construction and application of technical models complement and collaborate, collectively advancing the study’s development4,48,49.
The methods integrated within this study’s framework need to be detailed about public sports service quality. In terms of literature review, the study reviews the development and trends of service quality-related theories from numerous academic papers, policy documents, and industry reports. It further examines key factors influencing service quality and their mechanisms from different theoretical perspectives. For example, service management theory analyzes the impact of service process design and personnel management strategies on service quality; Customer satisfaction theory explores the role of public expectations and perception gaps in service quality evaluation. This provides solid theoretical guidance for subsequent research methods. Field research plays a crucial role in addressing the limitations of literature review. Additionally, the functional model’s application processes and rules in different public sports service scenarios need to be developed in detail50. For instance, in the service management scenarios of large sports venue, the functional model should dynamically allocate service resources and monitor service quality in real-time, based on factors such as venue event schedules, audience flow, and facility usage. In operation scenarios of community sports activity centers, the model should formulate personalized service plans and activity arrangements based on characteristics such as the community’s age structure, exercise preferences, and participation time patterns. By conducting in-depth analysis and rule formulation for different application scenarios, the study ensures the efficient operation of the functional model in diverse public sports service environments. Thus, it offers strong technical support and decision-making assurance for service quality improvement51.
Experimental design and performance evaluation
Datasets collection
Based on all of the above premises, this study conducts an in-depth investigation and analysis by using a dataset for model training, testing, and evaluation. The selected dataset is the “Comprehensive Evaluation Dataset of Urban Public Sports Services”, which has been carefully collated for in-depth analysis of various aspects of urban public sports services. The dataset covers multiple dimensions such as facility conditions, service processes, personnel quality, public feedback, and usage patterns to ensure the comprehensiveness and richness of the data. Through this dataset, the operation status, service efficiency, and public satisfaction of various sports facilities can be understood in detail, to provide solid data support for improving the quality of public sports service. Specifically, the dataset can reveal the specific operation of diverse sports facilities, evaluate the effectiveness of their services, and analyze the level of public satisfaction with the services. This information is of great significance for identifying the shortcomings and room for improvement in the service. In addition, the dataset also highlights comparative analysis between different cities and communities, helping to identify commonalities and differences in the provision of public sports services. This kind of comparative analysis not only reveals the successful experience and shortcomings of public sports services in different places but also offers a reference for other cities and communities to promote public sports services’ popularization and optimization.
Overall, the selected dataset covers various aspects of public sports services, such as long-term facility conditions, service processes, and user feedback, which are crucial for optimizing public sports services. By applying appropriate mathematical and statistical methods to analyze this dataset, potential patterns and trends within the historical data can be identified. For instance, through regression analysis, the relationship between different variables and service demand can be explored, leading to the development of predictive models. Before delving into the construction and application of the technical model, it is essential to explain the problem based on mathematical principles. This includes precisely defining the objective function, constraints, and variables related to the optimization of public sports service quality. For example, the objective function could be to maximize overall user satisfaction, considering constraints such as limited resources and service capacity. Moreover, the design of the technical model should account for the complexity of the problem. Given the multifaceted nature of public sports services, the model must incorporate various factors and their interactions. Additionally, the model should be scalable to ensure its effectiveness and efficiency when handling large datasets and complex scenarios. Regarding the data training and testing process, a detailed and systematic explanation is required. During the training phase, a portion of the dataset is used to train the model through specific algorithms. For instance, within the supervised learning framework, labeled data is utilized to adjust model parameters to minimize the loss function. In the testing phase, an independent subset of the dataset is used to evaluate the performance of the trained model, with accuracy and recall being employed to measure its effectiveness. Through this comprehensive approach to dataset analysis, problem formulation, and model training and testing, this study aims to propose practical optimization strategies. Therefore, it can effectively enhance the overall quality of public sports services, promote the successful implementation of national fitness programs, and ultimately encourage public participation in sports activities and improve overall health levels.
Experimental environment
A perfect experimental environment is constructed to ensure the supervised learning model’s effective application in the optimization of public sports service quality. The experimental environment mainly includes hardware and software. In terms of hardware, a high-performance computer server is used, equipped with enough memory and storage space to guarantee that the data processing and calculation needs are met during the model training process. Moreover, to accelerate the model’s training process, the Graphics Processing Unit (GPU) is also used to accelerate the device, which prominently improves the model’s training speed and efficiency. The experimental environment design of this study is outlined in Table 2.
Table 2 The experimental environment design.
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Parameters setting
Parameter setting is a crucial step in constructing and training supervised learning models to optimize the quality of public sports services. Parameter settings determine the complexity and learning ability of the model and directly affect the model’s performance. The design results of model parameters are exhibited in Table 3.
Table 3 Model parameters.
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Performance evaluation
(1) Model basic evaluation.
Based on the model design and data collection results mentioned above, this study evaluates the model using the dataset. During the evaluation process, text, images, videos, and comprehensive data are used to assess the model, as revealed in Fig. 3, which displays the results of the basic model evaluation using the dataset.
Fig. 3
figure 3
Results of model basic evaluation (a: text; b: images; c: videos; d: comprehensive data).
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Figure 3 depicts that this study conducts a comprehensive evaluation of the model using four different data formats. These data encompass various types of public sports service data, including user feedback, facility usage, and service quality evaluations, among others. Through processing and analyzing these data, the model demonstrates excellent performance. The evaluation results indicate that the model maintains an accuracy of over 88% for handling diverse types of data, which is quite satisfactory. Accuracy is a crucial metric for measuring whether the model’s predictions are correct, and an accuracy exceeding 88% implies that the model can accurately capture data features and make correct predictions. Moreover, the recall also exceeds 88%, further confirming the effectiveness of the model in identifying relevant data. Recall measures the model’s ability to find all relevant instances, and a high recall suggests that the model can identify as many relevant data points as possible. Thus, the model constructed in this study exhibits remarkable advantages and potential in optimizing public sports service quality. It can accurately handle and analyze various types of data, affording robust support for enhancing public sports service quality. These findings validate the feasibility and effectiveness of the model and lay a solid foundation for subsequent research and applications.
(2) Evaluation of model application effects.
In light of the foundational evaluation outlined above, this study proceeds to refine the model further, aiming to enhance its reliability and technical performance excellence through adjustment. The efficacy of the model’s application is subsequently assessed utilizing data from the Weibo network platform, with the outcomes of the model’s application performance evaluation depicted in Fig. 4.
Fig. 4
figure 4
Evaluation of model application effects (a is text, b is image, c is video, d is comprehensive data).
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In Fig. 4, the application effect of the designed model in the optimization of public sports service quality has been fully evaluated. Evaluation results reveal that the model’s accuracy is as high as 91%, and the recall is also stable at more than 90%, which fully demonstrates the model’s excellent performance in data processing and prediction. This result not only proves the model’s practicability and effectiveness but also furnishes robust technical support for further promoting the quality of public sports service.
Discussion
This study delves deeply into optimizing public sports service quality by constructing and evaluating supervised learning models. Regarding model performance, the experimental results demonstrate high accuracy and recall rates, thereby bolstering their practical utility within the realm of public sports services. From a practical standpoint, the application of this model opens up new avenues for enhancing public sports service quality. Through meticulous data analysis facilitated by the model, a more precise understanding of the public’s expectations and demands regarding sports services is attainable, thus providing a scientific underpinning for policy formulation and resource allocation. Additionally, the model’s application also aids in improving the utilization efficiency of public sports facilities, optimizing service processes, and enhancing user experiences, thus fostering the comprehensive advancement and evolution of the entire public sports service ecosystem.
Furthermore, with continuous technological advancements and the expansion of application scenarios, the optimization of public sports service quality faces more challenges and opportunities. Future research could focus on integrating the model with other advanced technologies such as big data analytics, cloud computing, and the Internet of Things to form more comprehensive and efficient solutions. Additionally, exploring the possibilities of the model’s application in more fields could contribute further to enhancing public sports service quality. In summary, this study provides new ideas and methods for optimizing public sports service quality by constructing and evaluating supervised learning models. While acknowledging extant challenges and limitations, it is anticipated that these issues will be effectively mitigated with continued research endeavors and technological advancements. Future investigations will persist in refining model performance and broadening application scopes, contributing more wisdom and strength to the improvement of public sports service quality. Service quality is closely linked to the concept of public sports. High-quality services enhance the sports experience and stimulate public participation. Public sports should adhere to the principles of fairness, inclusiveness, and health. This ensures that services benefit the public, promote the development of sports, and encourage national fitness, thereby creating a favorable sports environment for the people. Compared to Logacjov’s (2024) study52, this study demonstrates significant advantages in several aspects. In terms of research methodology, Logacjov’s study focuses solely on a single technology or theory. However, this study innovatively integrates public sports service theory, service quality theory, and applied theory to construct a multidimensional framework that comprehensively and thoroughly analyzes the issue. Although advanced studies may have made progress in model application, the supervised learning model predicts service demand with high accuracy and excels in the diversity of data processing. It effectively handles various data types, including text and images. Meanwhile, it maintains high accuracy and recall across different datasets and application scenarios, providing more reliable support and broader application prospects for optimizing public sports service quality.
Conclusion
Research contribution
This study constitutes a significant contribution to the realm of optimizing public sports service quality. Firstly, by implementing supervised learning models, machine learning methodologies have been successfully integrated into the domain of public sports services, facilitating precise prediction and quality optimization. This innovative endeavor not only enriches the methodological repertoire of public sports service research but also engenders fresh perspectives for associated fields of inquiry. Secondly, empirical analysis and model evaluation corroborate the efficacy of supervised learning models in enhancing public sports service quality. The models’ demonstrable accuracy and recall rates underscore their promise and relevance in practical contexts, furnishing decision-makers with robust decision support.
Future works and research limitations
While this study has achieved certain results, there are still limitations and areas to explore. Firstly, the scale and diversity of datasets need to be improved to enhance the model’s generalization ability. Secondly, plans involve further exploring the model’s specific applications in public sports services and focusing on new technological developments to improve service quality. Moreover, interdisciplinary cooperation and communication are crucial for jointly promoting the development of the field of public sports services. Ongoing endeavors will entail continued research aimed at refining model performance and broadening the horizons of application.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author Ying Yan on reasonable request via e-mail yanying@hncu.edu.cn.
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Funding
This work was supported by Scientific Research Project of the Education Department of Hunan Province (Excellent Young Scholars Project): Research on the Risk Control Mechanism and Safety Assurance Strategies for College Students' Outdoor Sports (Project Number: 24B0740).
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College of Physical Education, Hunan City University, Yiyang, 413000, Hunan, China
Ying Yan
School of Educational Studies, Universiti Sains Malaysia, Gelugor, Penang, 11800, Malaysia
Ying Yan
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Ying Yan
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Ying Yan: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration, funding acquisition.
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Correspondence to Ying Yan.
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Yan, Y. The optimization and impact of public sports service quality based on the supervised learning model and artificial intelligence. Sci Rep 15, 9923 (2025). https://doi.org/10.1038/s41598-025-94613-x
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Received:18 October 2024
Accepted:14 March 2025
Published:22 March 2025
DOI:https://doi.org/10.1038/s41598-025-94613-x
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Keywords
Artificial intelligence
Public sports
Sports service
Supervised learning
Quality optimization