Application of Sports Clustering Deconstruction Based on Neural Network

Abstract

Sports cluster analysis can mainly provide more teaching ideas for physical education. Teachers can make scientific and reasonable arrangements for the teaching plan according to the analyzed data results, so as to achieve better teaching purposes. However, due to various factors such as exercise time and course time conflict, this method cannot be widely used. The neural network had a good memory function, and it can be used to integrate physical education resources in sports. Then, a knowledge framework was formed by simulating a large number of human brain neuron structures, which can solve the problems existing in the motion cluster analysis to a certain extent. In this paper, the sports based on neural network is used to improve the problems existing in the practical teaching application of sports clustering analysis. A teaching system model based on motion recognition was established to improve motion cluster analysis and promote the implementation of data-based education. According to the experimental data obtained from the experimental test, people can know that the detection rate of the sports cluster analysis model is about 89.5%, and the average missed detection probability is about 5.5%.