Relation extraction aims to identify predefined semantic relationships between two entities within a sentence. Traditional graph neural network-based relation extraction methods generally use dependency trees to construct a graphical representation structure of the sentence. However， the graph structure constructed by dependency tree has limited expression ability and is unable to fully capture rich syntactic structure information of the target entity. To address these issues， a relation extraction method of Heterogeneous Graph ATtention network （HGAT） based on feature combination was proposed. Firstly， atomic features were extracted from the sentence， and composite features were obtained by combining these atomic features. Secondly， the composite features and relation labels were represented as two types of nodes on the heterogeneous graph to construct a “feature-relation bipartite graph”. Finally， a graph attention network was used to update the nodes dynamically to perform relation extraction. In this method， the composite features and the syntactic structure information in the sentence were utilized effectively， thereby enhancing the performance of relation extraction. Experimental results on ACE05 English dataset and SemEval-2010 task 8 dataset show that this method achieves F1-scores of 84.11% and 90.67%， respectively， demonstrating the effectiveness of the proposed method.