Recently,　clinical　phenotypic　semantic　information　has　begun　to　play　an　important　role　in　some　brain　network　classification　methods　based　on　deep　learning.　However,　most　current　methods　only　consider　the　phenotypic　semantic　information　of　individual　brain　networks　but　ignore　the　potential　phenotypic　characteristics　among　group　brain　networks.　To　address　this　problem,　we　present　a　deep　hashing　mutual　learning　(DHML)-based　brain　network　classification　method.　Specifically,　we　first　design　a　separable　CNN-based　deep　hashing　learning　to　extract　individual　topological　features　of　brain　networks　and　map　them　into　hash　codes.　Secondly,　we　construct　a　group　brain　network　relationship　graph　based　on　the　similarity　of　phenotypic　semantic　information,　in　which　each　node　is　a　brain　network,　and　the　properties　of　the　nodes　are　the　individual　features　extracted　in　the　previous　step.　Then,　we　adopt　a　GCN-based　deep　hashing　learning　to　extract　the　group　topological　features　of　the　brain　network　and　map　them　to　hash　codes.　Finally,　the　two　deep　hashing　learning　models　perform　mutual　learning　by　measuring　the　distribution　differences　between　the　hash　codes　to　achieve　the　interaction　of　individual　and　group　features.　The　experimental　results　on　the　three　commonly　used　brain　atlases　(AAL　Atlas,　Dosenbach160　Atlas,　and　CC200　Atlas)　of　the　ABIDE　I　dataset　show　that　our　proposed　DHML　method　achieves　optimal　classification　performance　compared　with　some　state-of-the-art　methods.　IEEE