Brain　network　classification　using　resting-state　functional　magnetic　resonance　imaging　(rs-fMRI)　is　an　effective　analytical　method　for　diagnosing　brain　diseases.　In　recent　years,　brain　network　classification　methods　based　on　deep　learning　have　attracted　increasing　attention.　However,　these　methods　only　consider　the　spatial　topological　characteristics　of　the　brain　network　but　ignore　its　proximity　relationships　in　semantic　space.　To　overcome　this　problem,　we　propose　a　novel　brain　network　classification　method　based　on　deep　graph　hashing　learning　named　BNC-DGHL.　Specifically,　we　first　extract　the　deep　features　of　the　brain　network　and　then　learn　a　graph　hash　function　based　on　clinical　phenotype　labels　and　the　similarity　of　diagnostic　labels.　Secondly,　we　use　the　learned　graph　hash　function　to　convert　deep　features　into　hash　codes,　which　can　maintain　the　original　semantic　spatial　relationships.　Finally,　we　calculate　the　distance　between　hash　codes　to　obtain　the　predicted　category　of　the　brain　network.　Experimental　results　on　ABIDE　I,　ABIDE　II,　and　ADHD-200　datasets　demonstrate　that　our　method　achieves　better　classification　performance　of　brain　diseases　compared　with　some　state-of-the-art　methods,　and　the　abnormal　functional　connectivities　between　brain　regions　identified　may　serve　as　biomarkers　associated　with　related　brain　diseases.