Existing static malware similarity measurement methods are affected by static anti-antivirus techniques, and the model features are either easily confused or fail to fully capture malware semantics. This study proposes a malware similarity measurement method called heterogeneous graph matching network-based similarity (HGMSim) to address the above problems. This method first uses the disassembly tool IDA Pro to extract a malware’s call graph, which is then abstracted into a heterogeneous graph to effectively capture the heterogeneous semantics of different function node types and their call relationships. Meanwhile, cross-graph edges are established for similar function nodes of the same type in two call graphs to mine the implicit neighbor semantics between nodes in different call graphs, and a heterogeneous graph matching network is constructed. Then, the study proposes a heterogeneous graph embedding method based on local node graph matching strategy and implements malware similarity measurement to solve the problem of difficulty in distinguishing malware with highly similar graph structures between different families. Finally, experimental results show that HGMSim performs best in malware similarity measurement.