As the field of artificial intelligence accelerates its growth, deep learning has emerged as an influential instrument for enhancing the productivity of sports coaching and the proficiency of athletes. This article aims to delve into how deep learning technology can be utilized to refine the techniques of Taekwondo practitioners and to assess its prospective significance. After introducing the basic principles of deep learning, convolutional neural network, and recurrent neural network, this paper analyses how to use deep learning to recognize movements, predict athlete’s movements, prevent injury risks, and conduct tactical and opponent analysis. Research shows that deep learning models can effectively learn from a large amount of data and provide immediate feedback, thus helping athletes optimize the training process, improve competitive performance, and reduce the occurrence of sports injuries. The findings of this paper provide scientific and technological support for Taekwondo training and competition and also provide a reference for the application of in-depth learning in other sports fields.