Depression　is　a　global　disease　that　is　harmful　to　people.　Traditional　identification　methods　based　on　various　scales　are　not　objective　and　accurate　enough.　Electroencephalogram　(EEG)　contains　abundant　physiological　information,　which　makes　it　a　new　research　direction　to　identify　depression　state.　However,　most　EEG-based　algorithms　only　extract　the　original　EEG　features　and　ignore　the　complex　spatiotemporal　information　interactions,　which　will　reduce　performance.　Thus,　a　more　accurate　and　objective　method　for　depression　identification　is　urgently　needed.　In　this　work,　we　propose　a　novel　depression　identification　model:　W-GCN-GRU.　In　our　proposed　method,　we　censored　six　sensitive　features　based　on　Spearman＇s　rank　correlation　coefficient　and　assigned　different　weight　coefficients　to　each　sensitive　feature　by　AUC　for　the　weighted　fusion　of　sensitive　features.　In　particular,　we　use　the　GCN　and　GRU　cascade　networks　based　on　weighted　sensitive　features　as　depression　recognition　models.　For　the　GCN,　we　creatively　took　the　brain　function　network　based　on　the　correlation　coefficient　matrix　as　the　adjacency　matrix　input　and　the　weighted　fused　sensitive　features　were　used　as　the　node　feature　matrix　input.　Our　proposed　model　performed　well　on　our　self-collected　dataset　and　the　MODMA　datasets　with　a　accuracy　of　94.72%,　outperforming　other　methods.　Our　findings　showed　that　feature　dimensionality　reduction,　weighted　fusion,　and　EEG　spatial　information　all　had　great　effects　on　depression　recognition.