Entity alignment (EA) tasks are pivotal in the integration of knowledge graphs. The most advanced research has introduced external knowledge (attribute texts, timestamps, image information, etc.) and multimodal methods, achieving relatively high accuracy. However, these methods often have a strong dependence on specific structures, which limits their applicability in the entity alignment tasks of knowledge graphs with different structures. Therefore, this study proposes a universal knowledge graph alignment approach that utilizes the information of shared entity, relationship, and graph structure of knowledge graphs which are called surface information as they can be directly observed in knowledge graphs. An embedding generation module and an alignment module are included in the proposed method, and the former uses the Transformer model to capture the inherent semantics of entities and the contributions of their neighbors while the latter achieves high-performance and stable alignment through a matching algorithm. Experiment results show that the proposed method has achieved the best performance in the alignment scenarios among multiple mainstream knowledge graphs, demonstrating stability and strong interpretability. The code used in this study can be obtained at https://github.com/zb1tree/TGEA.