For fusing map data from different sources, entity alignment is a key step, and its purpose is to determine equivalent entity pairs between different maps. Most of the existing entity alignment methods are based on graph embedding, which aligns by considering the structure and attribute information of the graph. However, they do not handle the interactive relationship between the two well and ignore the use of relationships and multi-order neighbor information. To solve the above problems, this study proposes an entity alignment method based on the fused structural and attribute attention mechanism model (FSAAM). The model first divides the graph data characteristics into attribute and structural channel data and then uses the attribute attention mechanism to learn attribute information. It adds the learning of relationship information to that of structural information and uses the graph attention mechanism to find the entities aligned with beneficial neighbor features. The Transformer encoder is introduced to better correlate information between entities, and the Highway network is utilized to reduce the impact of noise information that may be learned. Finally, the model applies the LS-SVM network to the similarity matrix of the learned structural channel and the attribute channel information, obtaining the integrated similarity matrix to achieve entity alignment. The proposed model is verified on three sub-datasets of the public data set DBP15K. Experimental results show that compared with the best results in the baseline model, its Hits@1 has increased by 2.7%, 4.3%, and 1.7% respectively, and Hits@10 and MRR have also improved, indicating that this model can effectively improve the alignment accuracy of entities.