Heterogeneous　Graph　Neural　Networks　(HGNNs)　have　emerged　as　powerful　tools　for　handling　heterogeneous　graphs.　However,　current　HGNNs　often　rely　on　meta-paths　or　intricate　aggregation　operations.　In　response,　we　introduce　a　heterogeneous　graph　neural　network　based　on　dual-view　graph　structure　augmentation,　which　consists　of　three　aggregation　processes.　By　leveraging　both　node　feature　information　and　graph　topology　structure　information,　our　method　selecting　homogeneous　neighbors　for　nodes　and　constructing　homogeneous　views.　Subsequently,　it　learns　node　representations　through　aggregation　on　these　views　and　the　original　graph.　Through　extensive　experiments　on　three　widely　used　real-world　heterogeneous　graphs,　our　method　demonstrates　its　simplicity　and　effectiveness,　and　outperforms　the　most　of　existing　models　in　the　task　of　heterogeneous　graph　node　classification.　©　2024　SPIE.