Heliyon (Feb 2024)
The optimization of college tennis training and teaching under deep learning
Abstract
To enhance the integration of deep learning into tennis education and instigate reforms in sports programs, this paper employs deep learning techniques to analyze tennis tactics. The experiments initially introduce the concepts of sports science and backpropagation neural networks. Subsequently, these theories are applied to formulate a comprehensive system of tennis tactical diagnostic indicators, encompassing construction principles, basic requirements, diagnostic indicator content, and evaluation indicator design. Simultaneously, a Back Propagation Neural Network (BPNN) is utilized to construct a tennis tactical diagnostic model. The paper concludes with a series of experiments conducted to validate the effectiveness of the constructed indicator system and diagnostic model. The results indicate the excellent performance of the neural network model when trained on tennis match data, with a mean squared error of 0.00037146 on the validation set and 0.0104 on the training set. This demonstrates the outstanding predictive capability of the model. Additionally, the system proves capable of providing detailed tactical application analysis when employing the tennis tactical diagnostic indicator system for real-time athlete diagnosis. This functionality offers robust support for effective training and coaching during matches. In summary, this paper aims to evaluate athletes' performance by constructing a diagnostic system, providing a solid reference for optimizing tennis training and education. The insights offered by this paper have the potential to drive reforms in sports programs, particularly in the realm of tennis education.
