LAUSR

The problems of low reliability and the high fitting degree of mutual information feature extraction of traditional sports to human health enhancement model are analyzed. We analyze and study the sports to human health enhancement model using data mining. The model consists of a data layer, a logic layer, and a presentation layer. Sports project data, real-time sports data, and health monitoring data are collected in the data layer, and the collected data are transmitted to the logic layer. The logical layer uses the dynamic difference feature classification algorithm of data mining to fuse human health data, extract the mutual information features of human health, and input the features into the long short-term memory (LSTM) neural network, which outputs the pattern recognition results of sports health after forward and reverse operations. The results of sports health pattern recognition are input into the display layer, and the enhancing effect of sports on human health is presented for users by constructing a model of sports on human health. The results show that the effect of sports on human health enhancement analyzed by the model in this paper is extremely accurate, which can significantly improve the health level of community residents and college students. When the number of data is about 600, it remains at about 0.05, indicating that this model has high reliability, and the fitting degree of mutual information feature extraction is up to 99.82%. It has certain practical application value.