Research on College Students' Physical Education Teaching and Training Load Control Methods Empowered by Intelligent Algorithms

Abstract

 With the improvement of people's living standards, sports and health have become the focus of attention, and sports monitoring systems are increasingly favored. In this paper, we design the training load control method for college students' sports teaching, propose the linear fitting sports data feature fusion method and the weighted matrix decomposition recommendation algorithm WSVDLFM based on the implicit semantic model to realize the intelligent monitoring of sports and personalized sports guidance recommendation. The experimental results show that the sports monitoring system based on the proposed data feature fusion method can effectively realize the instant sampling and fusion optimization of sports training data, and the data collection accuracy is always maintained above 92%. Meanwhile, the WSVDLFM algorithm is able to realize the accurate recommendation of sports guidance, and outperforms other comparative algorithms in both HR and NDCG. In addition, the proposed sports training method “Fun Interval Running” can maximize the efficiency of students' sports training under the premise of controlling students' training load through real-time monitoring of heart rate and pace, which is of great significance for college students' sports teaching.