Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network

Abstract

In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this paper proposes an operational risk prediction model based on an automatic encoder and convolutional neural networks. First, we use an automatic encoder to extract features of motion risk factors and obtain feature components that can highly represent risk. Secondly, based on the causal relationship between sports risk and risk characteristics, a convolutional neural network with a dual convolution layer and dual pooling layer topology is constructed. Finally, the sports risk prediction model is established by combining the auto-coded feature components with the topology of the convolutional neural network. Compared with other algorithms, the proposed method can effectively analyze and extract risk characteristics and has a high prediction accuracy. At the same time, it promotes the integration of sports science and computer science and provides a basis for the application of machine learning in the field of sports risk prediction.