# Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network
###### Abstract
To sum up, the research on the application of machine learning to sports risk mainly focuses on the identification of sports risk factors and the prediction of sports risk. The application of machine learning in sports risk prevention research not only makes up for the limitations of traditional statistics in dealing with multifactorial and nonlinear data but also can more completely show the original appearance before sports risk, providing a basis for clinical risk assessment and coaches to adjust training timely [9]. At present, the application of machine learning in sports risk prediction has attracted great attention in the field of sports science [10]. According to the existing research reports, the problems in the current research are as follows:  
1. The sample size is small. The application of machine learning to the study of sports risk requires a sufficient sample size. If the sample size is small, it is easy to have problems such as difficulty obtaining data features and poor generalization ability of the model [11]. However, the research in this field has just begun, and there are still few domestic studies in this field. There are factors such as insufficient investment in project funds and manpower, a lack of a unified risk information management system in sports team management, and a poor connection between athletes' training and the working modes of coaches and team doctors that lead to difficulties in data collection and the failure to obtain a sufficient sample size. 2. The integration of disciplines is insufficient. At present, the research in this field in China is relatively weak. Because the discipline connection is not close enough, scientific researchers with computer discipline backgrounds know very little about professional knowledge in the field of sports science, and scholars with sports discipline backgrounds cannot complete complex programming, which leads to problems such as the lack of interpretation of risk factors [12], making the development of machine learning in this field slow. Promoting the development of multidisciplinary cooperation will further promote the development of sports science and sports medicine. 3. The practical application rate is low. Using machine learning to analyze and mine the data generated in the training process of athletes can reveal the development trend of athletes' physical functions, assist coaches and team doctors in making decisions based on data, timely adjust the intensity and capacity of training, and avoid risks [13]. However, due to the late development of research in this field and the lack of research reports, it is not possible to better combine other application technologies to generate applications and apply them to practice. In view of the above shortcomings, this paper proposes a sports risk prediction model based on an automatic encoder and convolutional neural network to process and predict high-dimensional, large-sample sports risk data, aiming to promote the integration of sports and computer science and provide a basis for the application of machine learning in the field of sports risk prediction.