Entity alignment, a critical step in integrating multi-source knowledge graphs, plays a vital role in knowledge graph fusion. However, existing studies still exhibit limitations in effectively utilizing and integrating diverse entity feature information. To address this issue, an optimal transport-based multi-perspective feature fusion method was proposed for entity alignment. Specifically, during the feature learning phase, the semantic features of entity names and attributes were first extracted using pre-trained language models to mitigate structural sparsity and heterogeneity across knowledge graphs. Subsequently, the alignment performance was further enhanced by calculating string similarity between entity names. A relation-aware graph attention network was then introduced to capture multi-relational interactions between entities, thereby obtaining cross-graph structural features. In the feature fusion phase, the Wasserstein distance was employed to integrate multi-view features for alignment computation. The fused Gromov-Wasserstein distance was adopted to combine multi-view feature cost matrices with intra-graph structural cost matrices, generating final alignment results. Experimental evaluations on publicly available cross-lingual datasets (ZH-EN, JA-EN, FR-EN) demonstrate that the proposed method achieves Hits@1 scores of 0.989, 0.992, and 0.999, respectively. Compared to other models, the proposed method demonstrates superior performance in alignment.