Recently,　Brain-Computer　Interface　(BCI)　technology　has　been　applied　more　and　more　in　the　field　of　clinical　rehabilitation,　which　provides　an　effective　way　of　communication　for　patients　with　brain　disability　and　stroke.　Because　electroencephalogram　(EEG)　signals　are　extremely　complex　and　contain　many　redundant　signals,　the　recognition　effect　of　BCI　system　based　on　EEG　is　not　very　well.　The　purpose　of　this　paper　is　to　solve　the　problem　of　brain　intention　classification　in　brain-computer　interface　system,　which　combines　Deep　Separation　Convolutional　Neural　Network　(DSCNN)　and　Gate　Recurrent　Unit　(GRU)　network　to　classify　the　motor　imagination　task　of　EEG.　Firstly,　one-dimensional　timing　EEG　signals　are　transformed　into　two-dimensional　array,　and　the　temporal　and　spatial　features　of　EEG　signals　are　extracted　by　separate　convolution.　Then,　these　EEG　signals　containing　spatio-temporal　features　are　convolved　once　to　extract　spatial　features,　and　the　temporal　features　is　extracted　by　GRU.　Finally,　the　experiments　shows　that　the　final　intention　recognition　accuracy　reach　97.76%　via　the　open　physiological　motor　imagery　data　set　EEGMMIDB,　which　is　superior　to　some　advanced　research　methods　for　motor　imagery　task　recognition　at　present　and　helpful　to　restore　the　rehabilitation　ability　of　patients　with　brain　injury.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.