Psr Model with Intelligent Min-Max Estimation Deep Learning (Imin-Maxedl) for Public Service Evaluation of Sports Venues

Abstract

Sports venues play a pivotal role in fostering athletic excellence, community engagement, and social cohesion. From local recreation centers to iconic stadiums hosting international events, these facilities serve as hubs of activity, bringing together athletes, spectators, and enthusiasts from diverse backgrounds. The evaluation of sports venues is crucial for ensuring optimal functionality, service quality, and reputation within the community.

The Public Service Evaluation of Sports Venues, integrating the PSR (Psychological Skills Model) model, provides a comprehensive framework for assessing the effectiveness and quality of sports facilities. This paper presents a comprehensive framework for the public service evaluation of sports venues, integrating the PSR (Psychological Skills Model) model with Intelligent Min-Max Estimation Deep Learning (Imin-maxEDL). The PSR model serves as the foundation for assessing the effectiveness and quality of sports facilities across multiple dimensions, including performance, service quality, and reputation. Meanwhile, Imin-maxEDL employs advanced deep learning techniques to optimize the evaluation process, leveraging large datasets to extract nuanced insights and predictive analytics. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, demonstrating significant improvements in accuracy, efficiency, and predictive capabilities compared to traditional evaluation methods. the integration of Imin-maxEDL resulted in a 25% increase in accuracy in predicting service quality ratings, while also reducing evaluation time by 30%. Additionally, the PSR model combined with Imin-maxEDL achieved a 20% improvement in reputation assessment precision, leading to more informed decision-making by stakeholders.