The optimization of youth football training using deep learning and artificial intelligence.

***This study aims to improve the effectiveness and outcomes of youth football training by utilizing advanced deep learning and artificial intelligence (AI) technologies. Firstly, the relevant dimensions of deep learning and key training techniques of deep learning convolutional neural networks (CNNs) are analyzed. Secondly, a key point detection model for youth football training is constructed based on deep learning CNNs. Lastly, interviews are conducted with five technology companies and thirty sports teachers to analyze the application scenarios of AI in campus football training. The results show that the difficulty of key point visibility in the youth football training key point detection model is not high, and the model can provide relatively accurate results. The model achieved an accuracy of over 90% in critical point prediction, with low prediction errors for key points related to foot placement and curve positioning, all of which remained below 15%. Both companies and schools consider policy, cognitive, attitude, and technological factors as key factors for applying AI in campus football. In addition, hardware facility factors and other related factors also have an impact on the application of AI in campus football. The research results have practical reference significance for the intelligent development of youth football training and can promote the high-quality development of youth football in China.