Aiming at the problem of large information redundancy and low accuracy in human motion recognition in video sequences,a human motion recognition method is proposed based on key frame two-stream convolutional network. We construct a network framework consisting of three modules:feature extraction,key frame extraction,and spatial-temporal feature fusion.Firstly,the single-frame RGB image of the spatial domain video and the optical flow image superimposed in the time domain multi-frame are sent as input to the VGG16 network model to extract the depth feature of the video;secondly,the importance of each video frame is continuously predicted,then useful frames with sufficient information are pooled and trained by neural network to select key frames and discard redundant frames.Finally,the Softmax outputs of the two models are weighted and combined as the output result to obtain a multi-model fusion.The human body motion recognizer realizes the key frame processing of the video and the full utilization of the spatial-temporal information of the action.The experimental results on the UCF-101 public dataset show that,compared with the mainstream methods of human motion recognition,the proposed method has a higher recognition rate and relatively reduces the complexity of the network.