Sports Training Data Analysis and Model Research Based on Artificial Intelligence

This paper proposes a sports training data analysis and model research method based on artificial intelligence, focusing on the application of transfer learning algorithm in sports training. Through the collection and analysis of a large amount of sports training data, combined with time series analysis technology, a prediction model suitable for a variety of sports is designed and optimized. The model uses the transfer learning algorithm to transfer existing training experience between different sports, which improves the prediction accuracy and generalization ability of the model under data scarcity. The simulation results show that the prediction model using transfer learning can significantly improve the accuracy of training effect prediction in time series data analysis, and the error is controlled within 5%. In addition, by comparing different algorithms, the advantages of transfer learning algorithm in data sparse environment are proved. The research in this paper not only provides new data analysis and model optimization methods for sports training, but also provides theoretical support for the widespread application of artificial intelligence in the field of sports in the future.

I. Introduction

Since training data often has the characteristics of time series and the data differences between different sports are large, how to effectively use these data for intelligent analysis and prediction has become a key issue.