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In recent years, human action recognition has become a research hotspot in the field of computer vision. The research on Convolutional Neural Network has made great breakthroughs in the field of image classification and recognition, but human action recognition is based on video, containing information in both spatial domain and temporal domain. Aiming at human action recognition based on video, an improved Two-Stream ConvNet architecture is proposed. For the spatial domain, the single RGB image is fed into the VGGNet_16 model. For the temporal domain, the superposition of multi optical flow images is fed into the Flow_Net model. Finally, the softmax outputs of the two models are merged with the linear weighting to realize human action recognition. The experiments based on the JHMDB public database prove the effectiveness of the improved Two-Stream ConvNet architecture on human action recognition.