The methods of skeleton-based human action recognition (HAR) have gained widespread interest due to their robustness in capturing actions amidst changing environments and intricate backgrounds. Utilizing graph convolutional net- works (GCNs) to describe the human skeleton for HAR has been shown to achieve impressive performance. However, most GCN-based methods only consider the relationships between adjacent joints, overlooking the relationships between joints that are not connected by natural physical links. Therefore, we propose a novel method called Multi-level Graph Convolutional Network, abbreviated as ML-GCN. We have refined the original partitioning strategy, introducing three novel strategies to more effectively enhance the connectivity among distant joints. Additionally, we incorporate a multi-level graph convolutional network and a non-local temporal convolutional network to better extract spatio-temporal features. Experiments conducted on the NTU-RGB+D and Kinetics datasets demonstrate that our model achieves a certain improvement in accuracy.

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