Athlete Physical Fitness Evaluation and Training Recommendation System Based on Machine Learning

In response to the subjective nature and lack of precision in traditional methods of athlete physical fitness evaluation and training recommendations, this paper proposes an athlete physical fitness evaluation and training recommendation system based on machine learning. The system innovatively combines deep learning with traditional machine learning algorithms, designing a multimodal data fusion framework to extract features from various sources of athlete data, such as physiological indicators, training data, and competition results. This paper introduces a new model, DLRGD, which uses an improved dual-layer recurrent neural network (DL-RNN) to significantly enhance the feature capture capability of time-series data, while integrating a social characteristic analysis module based on graph neural networks (GNN) to explore the impact of team collaboration. The model's hyperparameters are automatically optimized through Bayesian optimization algorithms, further improving prediction accuracy. Experimental results show that the system significantly outperforms existing methods in terms of physical fitness evaluation accuracy and the rationality of training recommendations, providing scientific basis for formulating personalized training plans for athletes, which is of great significance for enhancing training efficiency and athletic performance.