Abstract: Aiming at the problems of existing human action recognition algorithms such as insufficient accuracy, large amount of calculation, and lack of deployment on edge devices, this paper proposes an edge-side lightweight human action recognition spatial temporal graph convolutional algorithm based on the Ascend processor. By designing an implicit skeletal connection method and constructing an implicit adjacency matrix, combined with the natural skeletal connection adjacency matrix, we create an explicit-implicit fusion spatial graph convolution. A spatial attention mechanism is added to the temporal dimension, enabling the model to focus on spatial features of joint positions across different frames. Furthermore, we design a temporal graph convolution to construct a spatiotemporal graph convolution. Additionally, the Ascend-Enisum operator is designed within the network to perform tensor fusion operations, reducing computational complexity and lightening the model. Experimental validation on the KTH dataset demonstrates that, compared to the classical single-stream ST-GCN algorithm, our model achieves a 22.28% reduction in computational cost while attaining a Top-1 accuracy of 84.17%, representing a 5% improvement. Based on this algorithm, we have designed the Ascend AI human action recognition system, which has been successfully deployed on edge devices for real-time human action recognition.