In view of the shortcomings that the 2S-AGCN model of the two-stream adaptive graph convolutional network ignores the long-distance information of features and channel dependence in human motion recognition, a dual attention mechanism is designed to improve the graph convolution module of the 2S-AGCN model to improve the accuracy. The dual attention mechanism includes the spatial attention mechanism and the channel attention mechanism. The spatial attention mechanism selectively focuses on the context. The channel attention mechanism is divided into two parallel modules. The first part improves the distinguishability of features. The second part preserves accurate location information while capturing the remote dependency of features. The results show that the model based on the two-stream adaptive graph convolutional networks 2S-AGCN, which incorporates the dual attention mechanism module, has improved Top-1 and Top-5 on the Kinetics dataset by 0.6 and 1.3 percentage points respectively, Top-1 on the CS and CV of NTURGB+D120 dataset by 1.2 and 0.5 percentage points respectively, and Top-1 on the CS and CV of NTURGB+D dataset by 0.2 and 0.1 percentage points respectively.