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This invention relates to the fields of graph neural networks and heterogeneous graph representation learning technology, specifically to a heterogeneous graph node classification method based on relation-aware label propagation. The method includes the following steps: first, based on a given heterogeneous network, then performing type-specific linear transformations on nodes of different types and projecting their features onto a common feature space; second, designing a relation-aware label propagation algorithm for target nodes in the heterogeneous network, generating pseudo-labels through relation subgraphs; and third, employing a two-layer aggregation strategy based on type attention to transmit information and effectively fuse multi-level information of nodes, resulting in richer and more accurate node feature representations. This invention significantly improves classification performance and robustness by designing a relation-aware label propagation method to obtain pseudo-labels, utilizing two-layer aggregation based on type attention for heterogeneous message transmission, and combining it with a multi-objective optimization strategy.