In tasks such as intelligent surveillance and human–computer interaction, developing rapid and effective models for human action recognition is crucial. Currently, Graph Convolution Networks (GCNs) are widely used for skeleton-based action recognition. Still, they primarily face two issues: (1) The insufficient capture of global joint responses, making it difficult to utilize the correlations between all joints. (2) Existing models often tend to be over-parameterized. In this paper, we therefore propose an Efficient Hierarchical Co-occurrence Graph Convolution Network (EHC-GCN). By employing a simple and practical hierarchical co-occurrence framework to adjust the degree of feature aggregation on demand, we first use spatial graph convolution to learn the local features of joints and then aggregate the global features of all joints. Secondly, we introduce depth-wise separable convolution layers to reduce the model parameters. Additionally, we apply a two-stream branch and attention mechanism to further extract discriminative features. On two large-scale datasets, the proposed EHC-GCN achieves better or comparable performance on both 2D and 3D skeleton data to the state-of-the-art methods, with fewer parameters and lower computational complexity, which will be more beneficial for application on computing resource-limited robot platforms.