Most of the current video playback speed recognition algorithms have poor extraction accuracy and many model parameters. Aiming at these problems， a dual-branch lightweight video playback speed recognition network was proposed. First， this network was a Three Dimensional （3D） convolutional network constructed on the basis of the SlowFast dual-branch network architecture. Secondly， in order to deal with the large number of parameters and many floating-point operations of S3D-G （Separable 3D convolutions network with Gating mechanism） network in video playback speed recognition tasks， a lightweight network structure adjustment was carried out. Finally， the Efficient Channel Attention （ECA） module was introduced in the network structure to generate the channel range corresponding to the focused content through the channel attention module， which helped to improve the accuracy of video feature extraction. In experiments， the proposed network was compared with S3D-G， SlowFast networks on the Kinetics-400 dataset. Experimental results show that with similar accuracy， the proposed network reduces both model size and model parameters by about 96% compared to SlowFast network， and the number of floating-point operations of the network is reduced to 5.36 GFLOPs， which means the running speed is increased significantly.