Entity alignment, which identifies equivalent entities across source and target knowledge graphs, is essential for constructing comprehensive knowledge bases. Although TransE-based methods show effectiveness, they struggle to capture complex relations due to limited embedding expressiveness. The advanced TransD model uses dynamic mapping matrices but remains constrained by static relation modeling, semantic ambiguity, and insufficient generalization for low-frequency relations. To address these challenges, this paper proposes a Multi-Granularity Dynamic Projection Entity Alignment (MG-DPEA) model by integrating fine-grained and coarse-grained relation projections. Firstly, the fine-grained layer retains TransD’s entity relation collaborative projection to capture specific relational features. Secondly, the coarse-grained layer uses hierarchical semantic clustering based on WordNet and the Stick-Breaking algorithm, grouping semantically similar relations that share projection matrices. Finally, an adaptive fusion unit dynamically integrates these multi granularity knowledge graphs. Extensive experiments demonstrate that MG-DPEA significantly improves alignment performance, especially for low-frequency relations, providing an interpretative and efficient approach for knowledge graph fusion under resource-constrained scenarios.