Research on Sports Action Recognition and Intelligent Feedback System Based on Convolutional Neural Network

With the vigorous development of sports industry, the demand for efficient and accurate sports action analysis technology is increasing. The traditional method of motion recognition is limited by the subjectivity and inefficiency of manual observation, and the excellent performance of Convolutional Neural Network (CNN) in the field of image processing provides a new idea for its application in sports motion recognition. The system framework proposed in this paper includes data preprocessing, CNN model design, motion recognition algorithm and intelligent feedback module. The video data collected by high-definition cameras are preprocessed and then input into a specially designed CNN model, which uses multi-layer feature extraction to realize high-precision action classification. The experimental findings indicate that the system's recognition accuracy is typically above 85% across various sports, with accuracy levels reaching between 90% and 94% for common actions like sprint running and freestyle swimming. In addition, the intelligent feedback system can provide personalized evaluation, technical guidance and improvement suggestions according to the recognition results, and meet the needs of athletes in different scenarios through real-time voice prompts or detailed reports. This study enhances both the precision and real-time performance of sports action recognition, while also offering athletes timely and practical technical feedback. This has significant implications for advancing the intelligent development of the sports industry.