The Analysis of Deep Learning-based Football Training under Intelligent Optimization Technology

Abstract

This work aims to optimize college football training using deep learning techniques, addressing the inefficiencies, difficulty in action recognition, and insufficient data analysis present in current training methods. An intelligent optimization system combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is proposed to tackle these challenges. Compared to traditional single models, the Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) architecture remarkably improves the efficiency and accuracy of processing training data by leveraging the strengths of spatial features and temporal sequence features. The experimental results show that CNN-RNN model is significantly superior to the traditional 3D CNN model and other advanced models, such as Transformer, Long Short-Term Memory (LSTM), Bidirectional LSTM and Gated Recurrent Unit (GRU), in key indicators such as accuracy, precision, recall and F1 score. Specifically, CNN-RNN model achieves 92.5% accuracy, 91.2% precision, 93.1% recall and 92.1% F1 score. The lowest training loss rate is 0.24, which is significantly better than other models. In addition, the introduced data balance strategy effectively improves the prediction performance of a few categories (such as foul and yellow card events) through oversampling, undersampling and weighted loss function, and further enhances the generalization ability and practicability of the model. Future research focuses on expanding the dataset, further improving the model’s generalization ability, and exploring its application in real training scenarios.