Research on the Evaluation Model of Sports Public Services Based on Artificial Intelligence Algorithms

Public sports services are an essential part of the current government service program. They are essential for strengthening the body, raising public satisfaction levels with public services, and improving welfare. These services encompass a wide range of initiatives, programs, and facilities aimed at promote physical activity, enhancing community health, and fostering social cohesion through sports. The study’s goal is to explore an artificial intelligence (AI) algorithm-based technique for evaluating sports public services. In this study, we proposed a novel Garra Rufa fish fine-tuned flexible deep neural network (GRF-FDNN) to evaluate the sports public services in stadiums. The information gathered from several sources, including maintenance records, facility utilization, and service availability, was combined to provide service performance statistics. The data were preprocessed using normalization for the obtained data and the characteristics are extracted from the normalized data using PCA. The proposed model assesses and optimizes key service indicators, including user satisfaction, service accessibility, and operational efficiency using GRF and FDNN to create a robust framework for evaluating and optimizing sports public services in stadiums. The proposed method is implemented using Python software. The result demonstrated that the GRF-FDNN method outperforms the sports public services in stadiums compared to other traditional algorithms. This study offers a framework for utilizing AI to improve sports public service systems for policymakers and service providers.