Entity alignment (EA) aims to identify equivalent entities across different knowledge graph (KG). Embedding-based EA methods still have several limitations, listed below. First, the heterogeneous structures within KGs are not fully modeled. Second, the utilization of text information is constrained by word embeddings. Third, alignment inference algorithms are underexplored. To address these limitations, this study proposes a heterogeneous graph attention network for entity alignment (HGAT-EA). HGAT-EA consists of two channels: one for learning structural embeddings and the other for learning character-level semantic embeddings. The first channel employs a heterogeneous graph attention network (HGAT), which fully leverages heterogeneous structures and relation triples to learn entity embeddings. The second channel utilizes character-level literals to learn character-level semantic embeddings. HGAT-EA incorporates multiple views through these channels and maximizes the use of heterogeneous structures through HGAT. HGAT-EA introduces three alignment inference algorithms. Experimental results validate the effectiveness of HGAT-EA. Following these results, we provide detailed analyses of the various components of HGAT-EA and present the corresponding conclusions.