Deep learning needs to make full use of the spatio-temporal information of the actions in the video to perform action recognition. In order to make full use of the spatio-temporal features in video to improve the accuracy of action recognition and save relevant information at a lower cost

a spatio-temporal feature fusion action recognition framework using sparse sampling scheme is proposed. The framework uses the sparse sampling to obtain the RGB images and optical flow images of videos

and respectively sends the spatio-temporal features to the VGG-16 network to extract the spatio-temporal features

then the spatial CNN and the temporal CNN are merged to extract the fused spatio-temporal features middle level

finally the fused spatio-temporal features are sent to the C3 D CNN to performs action recognition. The experimental results of two datasets in HMDB51 and UCF101 show that the framework can make full use of the temporal information and spatial information of the video to achieve higher action recognition accuracy.