<p>Due to the complementary strengths of language models and knowledge graphs, question-answering systems that combine both techniques have emerged. However, existing models suffer from issues such as introducing excessive misleading information and an unreasonable distribution of heterogeneous representations, which negatively affect answer accuracy and reasoning efficiency. To address these problems, we propose a question-answering model based on split-hop information propagation and heterogeneous representation alignment. First, we train a graph attention network using the split-hop information propagation method along multi-hop reasoning paths, enhancing the relevance between extracted sub-graphs and the question context while allowing the network to learn differences across reasoning hops. Next, we align the heterogeneous representations of text and knowledge graph through post-training, ensuring the pre-trained representations fall into a reasonable parameter distribution. We validate the effectiveness and generalizability of our model on the CommonsenseQA and OpenBookQA datasets in the commonsense domain, as well as the MedQA-UMSLE dataset in the biomedical domain, achieving accuracy improvements of 0.5%, 0.6%, and 1.2% over baseline models of the same type, respectively.</p>