Entity alignment technology aims to identify and match items that refer to the same entity across different knowledge graphs. It plays a crucial role in the integration of knowledge graphs and demonstrates broad application potential and significant practical value in multiple fields such as knowledge completion and social network analysis. With the continuous evolution of entity alignment methods based on knowledge representation learning, researchers begin to explore the use of multiple information dimensions among entities to calculate similarity, thereby evaluating the similarity between source and target entities. Nonetheless, some of the attribute information of entities is not fully exploited in existing methods, especially the thematic information within entity attributes. By using topic models, more prominent semantic connections between entities can be identified. Focusing on this research, with the thematic information of entity attributes as the core, this study proposes an entity alignment framework called EAGT (knowledge graph entity alignment via graph convolutional networks with biterm topic model), which aligns entities by combining entity topics with graph convolutional neural networks. To verify the effectiveness of the proposed method, experiments are conducted on open-source datasets. The results show that EAGT achieves performance improvements in most cases.