Heterogeneous graph representation learning method based on common attention

The invention provides a heterogeneous graph representation learning method based on common attention, and the method comprises the steps: determining a local attention score of a neighbor node of a target element path based on a preset attention parameter vector, a first hidden feature vector, a second hidden feature vector and a semantic fusion vector of the target element path; determining a global attention score of the neighbor node of the target meta-path based on the local attention score, the number of other meta-paths corresponding to the neighbor node of the target meta-path and a preset hyper-parameter; determining an updated semantic representation vector of the target node based on the global attention score, the first hidden feature vector, the second hidden feature vector and the semantic fusion vector; and determining the attention weight of each target element path based on the updated semantic representation vector of the node in each target element path in the heterogeneous graph, and deter