Entity alignment aims to find equivalent entities located in different knowledge graphs and is an important step for knowledge fusion. Currently, mainstream entity alignment methods are those based on graph neural networks. However, they often rely too much on the structural information of graphs, as a result of which models trained on specific graph structures cannot be applied to other graph structures. Meanwhile, most methods fail to fully utilize auxiliary information, such as attribute information. In response, this study proposes an entity alignment method based on a graph attention network and attribute embedding. The method uses the graph attention network to encode different knowledge graphs, introduces an attention mechanism from entity application to attribute, and combines structure embedding and attribute embedding in the alignment stage to improve the effect of entity alignment. The proposed model is verified on three real-world datasets, and the experimental results show that the proposed method outperforms the benchmark methods for entity alignment by a large margin.