In recent years, Graph Convolutional Networks have exhibited outstanding performance in the field of skeleton-based action recognition. Nevertheless, existing GCN-based methods suffer from limitations in modeling complex node correlations and insufficient utilization of complementary information between modalities. To address these issues, this paper proposes a Multi-Semantic Dynamic Graph Convolutional Network (MSD-GCN). This network adopts a joint-bone fused dual-stream architecture, processing joint and bone modality data in parallel. The dual-stream network consists of multiple Multi-Semantic Dynamic Graph Convolution (MSD-GC) operators, multiple Multi-Scale Temporal Convolution (MS-TC) operators, and a Joint-Bone Cross-Modal Contrastive Learning (JB-CMCL) module. Specifically, the MSD-GC operator reconstructs high semantic granularity partitions through a Semantic-Aware Hierarchical Graph (SH-Graph) and executes in parallel a Cross-Semantic Space Modeling (CSSM) module to capture global joint correlations and a Local Geometry Modeling (LGM) module to capture subtle motion features. The JB-CMCL module guides feature fusion and enhancement between joint and bone modalities within the dual-stream network through cross-modal feature alignment and hard sample discrimination mechanisms, thereby improving the model"s fine-grained recognition capability. Extensive experiments were conducted on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. The results demonstrate that the proposed components and the overall network exhibit superior performance, effectively recognizing ambiguous actions. Compared with state-of-the-art methods, our model shows strong competitiveness.