With the rapid development of artificial intelligence and its deep integration into the field of sports training, intelligent technologies are increasingly being applied to enhance training efficiency and athlete safety. This paper aims to design and implement an intelligent recognition system for athletes’ wrong movements based on artificial intelligence. Relying on integrated sensors and deep learning technology, the system can monitor and correct athletes’ movements in real time. The system adopts data collection, preprocessing, feature extraction, model training and real-time feedback to accurately identify athletes’ wrong postures and movements, and give appropriate correction suggestions. Support vector machine (SVM) and convolutional neural network (CNN) models are used to analyze the data related to athletes’ acceleration, angular velocity and images. The analysis achieves an accuracy rate of 94%. The response time of the system in various test environments is maintained between 150 and 180 ms, and the stability and adaptability are strong. The test results show that the system not only has the ability to efficiently identify wrong movements, but also can provide athletes with personalized training feedback to reduce the risk of injury during training implementation. After further optimizing the algorithm and expanding the data set, the system is expected to show stronger performance in more complex sports scenes.