Depression　has　gradually　become　the　most　common　mental　disorder　in　the　world.　The　accuracy　of　its　diagnosis　may　be　affected　by　many　factors,　while　the　primary　diagnosis　seems　to　be　difficult　to　define.　Finding　a　way　to　identify　depression　by　satisfying　both　objective　and　effective　conditions　is　an　urgent　issue.　In　this　paper,　a　strategy　for　predicting　depression　based　on　spatiotemporal　features　is　proposed,　and　is　expected　to　be　used　in　the　auxiliary　diagnosis　of　depression.　Firstly,　electroencephalogram　(EEG)　signals　were　denoised　through　the　filter　to　obtain　the　power　spectra　of　the　three　corresponding　frequency　ranges,　Theta,　Alpha　and　Beta.　Using　orthogonal　projection,　the　spatial　positions　of　the　electrodes　were　mapped　to　the　brainpower　spectrum,　thereby　obtaining　three　brain　maps　with　spatial　information.　Then,　the　three　brain　maps　were　superimposed　on　a　new　brain　map　with　frequency　domain　and　spatial　characteristics.　A　Convolutional　Neural　Network　(CNN)　and　Gated　Recurrent　Unit　(GRU)　were　applied　to　extract　the　sequential　feature.　The　proposed　strategy　was　validated　with　a　public　EEG　dataset,　achieving　an　accuracy　of　89.63%　and　an　accuracy　of　88.56%　with　the　private　dataset.　The　network　had　less　complexity　with　only　six　layers.　The　results　show　that　our　strategy　is　credible,　less　complex　and　useful　in　predicting　depression　using　EEG　signals.