Abstract: Graph neural networks can effectively learn network semantic information and have achieved good performance on node classification tasks, but still facing challenge: how to make the best of rich heterogeneous semantic information and comprehensive structural information to make node classification more accurate. To resolve the above challenge, based on the graph convolution operation, HNNCF (heterogeneous network node classification framework) is proposed to solve the node classification task in heterogeneous networks, including two steps of heterogeneous network reduction and graph convolution node classification. Firstly, through the designed heterogeneous network reduction rules, HNNCF simplifies a heterogeneous network into a semantic homogeneous network and retains semantic information of the heterogeneous network through relation representations between nodes, reducing the complexity of network structure modeling. Then, based on the message passing framework, a graph convolution node classification method is designed to learn network structure information on the semantic homogeneous network, such as neighbor weights without 1-sum constraint, to discover the differences of relations and neighbor semantic extraction. Finally, heterogeneous node representations are generated and used to classify nodes to identify node category labels. Experiments on three public node classification datasets show that HNNCF can make the best of heterogeneous semantic information and effectively learn network structure information such as reasonable neighbor weights to improve the performance of heterogeneous network node classification.