The data analysis of sports training by ID3 decision tree algorithm and deep learning

In order to improve the accuracy and efficiency of sports training data analysis, this paper proposes an optimized analysis model by combining Iterative Dichotomiser 3 (ID3) decision tree algorithm and deep learning model. As an important scientific tool, sports training data analysis aims to provide decision support for athletes and coaches, optimize training programs and improve sports performance through accurate data mining and model prediction. Traditional analysis methods have shortcomings in dealing with complex and multidimensional data, while analysis methods based on artificial intelligence can significantly improve the ability of feature extraction and prediction. Based on this background, this paper comprehensively evaluates the performance of each model in different dimensions by comparing six key indicators: mean square error (MSE), mean absolute error (MAE), information gain, feature importance, sports performance improvement rate and training target achievement rate. The experimental results show that the optimized model has the best MSE, and its MSE is only 1.05 under the information gain. It is significantly better than Extreme Gradient Boosting (XGBoost) of 1.48 and Capsule Networks (CapsNets) of 1.25. In terms of MAE, the minimum error of the optimized model is 0.65, while the maximum error of XGBoost is 1.11. In terms of information gain and feature importance, the optimization model is also outstanding, with the highest information gain of 1.02 and the feature importance maintained at a high level of 0.94 in many dimensions. Meanwhile, the optimized model is superior to other models in sports performance improvement rate (up to 6.71) and training target achievement rate (up to 78.32%). Therefore, this paper has certain reference significance to the field of sports training data analysis.