With the continuous development of modern sports,the types of Olympic Games are becoming more and more complex,which poses a new challenge to the cl assification of sports video.The existing manual classification methods cannot effectively distinguish between group sports (ball games) and individual sports (track and field) videos,so as to carry out large-scale automatic classificati o n storage.However,in order to effectively reuse these video files,it is neces sary to classify and store them.The main purpose is to improve the utilization of resources.Aiming at the low efficiency of manual classification methods,thi s paper studies the content classification of Olympic sports video,and proposes a video classification method based on key frame feature extraction and support vector machine (SVM).With the sports video of the 31st Olympic Games as the da ta set,the key frames of each video are extracted and summarized,and the corre sponding feature vectors of the video are calculated by Lagrange-Gauss transfor m .The feature vectors are used as the input of the SVM classifier to classify th e sports video.The experimental results show that for any Olympic video,the pr oposed method can achieve an average correct classification rate of more than 70%,while the error classification rate is always less than 10%.In particular,f or shooting videos in the Olympic Games,the average correct classification rate approaches to 90%,which shows the effectiveness of the proposed method.