Depression　is　a　common　but　easily　misdiagnosed　disease　when　using　a　self-assessment　scale.　Electroencephalograms　(EEGs)　provide　an　important　reference　and　objective　basis　for　the　identification　and　diagnosis　of　depression.　In　order　to　improve　the　accuracy　of　the　diagnosis　of　depression　by　using　mainstream　algorithms,　a　high-performance　hybrid　neural　network　depression　detection　method　is　proposed　in　this　paper　combined　with　deep　learning　technology.　Firstly,　a　concatenating　one-dimensional　convolutional　neural　network　(1D-CNN)　and　gated　recurrent　unit　(GRU)　are　employed　to　extract　the　local　features　and　to　determine　the　global　features　of　the　EEG　signal.　Secondly,　the　attention　mechanism　is　introduced　to　form　the　hybrid　neural　network.　The　attention　mechanism　assigns　different　weights　to　the　multi-dimensional　features　extracted　by　the　network,　so　as　to　screen　out　more　representative　features,　which　can　reduce　the　computational　complexity　of　the　network　and　save　the　training　time　of　the　model　while　ensuring　high　precision.　Moreover,　dropout　is　applied　to　accelerate　network　training　and　address　the　over-fitting　problem.　Experiments　reveal　that　the　1D-CNN-GRU-ATTN　model　has　more　effectiveness　and　a　better　generalization　ability　compared　with　traditional　algorithms.　The　accuracy　of　the　proposed　method　in　this　paper　reaches　99.33%　in　a　public　dataset　and　97.98%　in　a　private　dataset,　respectively.