Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

Abstract

In order to improve the performance of sports performance prediction, based on computational learning algorithms, this article builds a sports performance prediction model based on ensemble learning algorithms under the guidance of machine learning ideas. Moreover, this article applies the cascade principle to improve the accuracy of the model and determines the cascade structure, studies the characteristics of spatio-temporal sequence data and the modeling methods of spatio-temporal sequence models, and combines the idea of selective integration learning to improve the spatio-temporal neural network model. In addition, this paper uses the L1 regularization method to sparsely weight and combine multiple STELM models to achieve selective integration. Finally, this paper designs experiments to predict the performance of this model in sports performance prediction. The research results show that the prediction results of the sports performance prediction model constructed in this paper are accurate.