Multi-modal entity alignment (MMEA) is a crucial technique for integrating multi-source heterogeneous multi-modal knowledge graphs (MMKGs). This integration is typically achieved by encoding graph structure and calculating the plausibility of multi-modal representation between entities. However, existing MMEA methods tend to directly employ pre-trained models and overlook the fusion between modalities as well as the fusion between modal features and graph structures. To address these limitations, this study proposes a novel approach called relation-aware multi-subgraph graph neural network (RAMS) for obtaining multi-modal representation in the context of entity alignment. RAMS utilizes a multi-subgraph graph neural network for fusing modality information and graph structure to derive entity representation. The alignment results are subsequently obtained through cross-domain similarity calculation. Extensive experiments demonstrate that RAMS outperforms baseline models in terms of accuracy, efficiency, and robustness.