In order to further improve the accuracy of human action recognition and fully explore the spatiotemporal features of action sequences, a graph convolution action recognition method based on spatiotemporal feature fusion and attention mechanism is proposed. The spatial attention map convolution is used to refine the topology to capture the correlation features of the joints under different motion types,and the time convolution structure is extended by the time domain multi-scale convolution module to capture the multi-scale time features. A multi-level feature fusion module is constructed,which takes the initial feature and the convolution output feature of the time-domain multiscale graph as the module input,and uses a two-branch structure to obtain the global and local channel features respectively. On this basis,a limb attention mechanism is proposed to divide the human topological structure and calculate the attention weights in the channel dimension respectively to enhance the model's ability to pay attention to local action features. The experimental results show that the recognition accuracy is 93.0% and 96.9% in CS and CV evaluation mode of NTU RGB+D data set,and 89.8% and 91.1% in X-Sub and X-Set evaluation mode of NTU RGB+D 120 data set,respectively. The recognition accuracy is higher than that of ST-GCN,CTR-GCN and other models.