This paper introduces a dynamic tennis analysis system based on deep learning, designed to achieve real-time, high-precision analysis. The system adopts a layered architecture, integrating improved object detection, tracking, and event recognition algorithms. Innovations and optimizations have been made in key processes such as video preprocessing, feature extraction, object detection and tracking, and event recognition. Test results show that the system achieves real-time analysis in high-resolution video processing and achieves high accuracy in object detection and event recognition. Compared to existing commercial products, this system has significant advantages in terms of real-time performance, accuracy, and scalability. The research not only advances sports analysis technology but also provides a powerful tool for enhancing viewer experience and supporting professional training. Future research will focus on optimizing algorithm efficiency, expanding application scenarios, and exploring AI-assisted strategy analysis.