With advances in deep learning technology, the study and application of Human Action Recognition (HAR) systems in competitive sports have evolved, becoming more profound and diverse. These systems have demonstrated the potential to enhance athletes’ training and competitive performance while introducing innovation and progress into sports education and entertainment. This paper addresses the practical needs of sports training by designing a HAR system tailored to competitive sports scenarios, subsequently analyzing its recognition performance and applied models. The primary contribution of this paper lies in its exploration of HAR technology through Convolutional Neural Networks (CNNs) in the context of competitive sports. It systematically investigates and applies HAR requirements in competitive settings. Additionally, this paper evaluates the real-world performance of AlexNet and GoogleNet, constructs a CNN-based HAR system, and assesses its capabilities using publicly available datasets. These efforts provide valuable insights and technical support for the implementation of CNN-based HAR technology in competitive sports and other related fields, offering both academic and practical applications. The results indicate that different models achieve recognition accuracies of 94.45%, 95.04%, 93.01%, 93.23%, and 90.54% under five distinct decision-level fusion equations (A# ∼E#, respectively). Following fine-tuning and optimization, the recognition accuracy of AlexNet, GoogleNet, and ResNet networks significantly improved, with the model achieving a remarkable 99.94% accuracy in recognizing and analyzing the same athlete. In comparison to alternative algorithms, the designed HAR system prioritizes immediacy and interactivity while offering superior accuracy and broader application potential. It successfully fulfills its intended function, accurately recognizing human actions from video images, thereby proving invaluable for research in competitive sports.