Entity alignment is a critical task for integrating multi-source knowledge graphs, aiming to identify semantically equivalent entities across heterogeneous graphs. Existing methods typically rely on an entity's neighborhood structure or pretrained semantic information for representation learning, but they perform poorly in complex heterogeneous scenarios and generally fail to effectively utilize multi-hop path information. To address these limitations, we propose the entity alignment with path-integrated structure-aware attention (PISA). This model first extracts semantically representative multi-hop paths between entities to construct a path-triple-based extended graph structure. Then, a structure-aware multi-head attention mechanism is introduced to learn entity representations from the fused graph, enabling the model to more comprehensively capture semantic relations between entities across complex structures and diverse relations. On the DBP-15K FR-EN, JA-EN, and ZH-EN subsets, PISA’s Hits@1 improves over the baseline by 1.90%, 0.76%, and 1.79%, respectively. On the WK31-15K EN-DE (V1), EN-DE (V2), EN-FR (V1), and EN-FR (V2) subsets, PISA’s Hits@1 improves over the baseline by 1.78%, 2.01%, 1.46%, and 1.27%, respectively, which demonstrates its effectiveness and stability in heterogeneous knowledge-graph entity alignment.