Graph Convolutional Networks (GCNs) have shown great potential in skeleton-based human action recognition. However, due to the diversity and complexity, modeling human actions as general graphs and capturing discriminative spatial–temporal motion patterns is challenging. Besides, the inevitable interference, especially occlusion, impairs the robustness of existing methods that depend on complete skeletons. To solve these problems, we propose a Multi-Granular Spatial-Temporal Synchronous Graph Convolutional Network (MSS-GCN). Firstly, we investigate three partition strategies: attribute, activity, and mixed partition strategy to optimize the weight-sharing mechanism of GCNs, which facilitates the novel Extended Adaptive Graph Convolution (EAGC) module. Secondly, we elaborate on a Multi-sliced Spatial–temporal Graph (MSTG) for multi-granular action modeling. Thirdly, we present a Synchronized Slice Encoder (Syn-STE) to simultaneously embed spatial and temporal action patterns. Then, we design Multi-granular Spatial–temporal Encoders (Multi-STE) with multi-branch Syn-STE to generate multi-granular context. The extensive experiments verified that MSS-GCN is more robust and outperforms benchmarks on NTU-RGB+D, NTU-RGB+D 120, and NW-UCLA datasets.