Since existing work on the task of fake news detection frequently ignores the semantic sparsity of news text and the potential relationships between rich information, which limits the model’s capacity to understand and recognize fake news, this study proposes a fake news detection method based on heterogeneous subgraph attention networks. Heterogeneous graphs are constructed to model the abundant features of fake news, such as text, party affiliation, and topic of news samples. The heterogeneous graph attention network is constructed at the feature layer to capture the correlations between different types of information, and a subgraph attention network is constructed at the sample layer to mine the interactions between news samples. Moreover, the mutual information mechanism based on self-supervised contrastive learning focuses on discriminative subgraph representations within the global graph structure to capture the specificity of news samples. Experimental results demonstrate that the method proposed in this study achieves about 9% and 12% improvement in accuracy and F1 score, respectively, compared with existing methods on the Liar dataset, which significantly improves the performance of fake news detection.