Predictive Analysis and Simulation of College Sports Performance Fused with Adaptive Federated Deep Learning Algorithm

Abstract

With the widespread use of intelligent teaching, data containing student performance information continues to emerge, and artificial intelligence technology based on big data has made a qualitative leap. At present, the prediction of college students’ sports performance is only based on the past performance, and it does not reflect the student’s training effect very well. In order to solve these problems, this paper puts forward the analysis and simulation of college sports performance fusion with adaptive federated deep learning algorithm, aiming to study the influencing factors of student sports performance and suggestions for improvement. This paper uses an adaptive federated learning method and a personalized federated learning algorithm based on deep learning and then proposes a student performance prediction method. These methods integrate the quantitative methods of motor skill assessment and establish standards for college students, which are good standards for evaluating college students’ sports skills. This paper adopts the performance prediction framework and then establishes the sports performance prediction model. Through the analysis of sports performance analysis examples, it is concluded that the model proposed in this paper can accurately predict the student’s sports performance, and the average accuracy rate of each sports item has reached 91.7%.