To　address　the　problem　that　some　source　domain　samples　that　are　difficult　to　transfer　disturb　the　target　domain　data　distribution　due　to　the　difference　in　transfer　value　between　different　motor　imagery　electroencephalogram　(MI-EEG)　sample　data,　and　that　the　model　has　poor　feature　extraction　and　classification　performance　when　adapting　to　different　motor　imagery　datasets,　this　paper　improves　the　conditional　domain　adversarial　network　(CDAN)　method　introduced　by　domain　generalization　technology,　and　proposes　a　conditional　domain　adaptation　network　based　on　sample　weight　(SW-CDAN)　method.　This　method　makes　the　entropy　output　by　the　domain　discriminator　as　the　sample　weight,　which　is　used　to　adjust　the　classification　loss　during　the　model　training　process,　so　that　the　model　can　extract　transferable　features　from　the　common　features　of　the　data,　thereby　enhancing　the　model＇s　category　prediction　ability　and　model　generalization　ability.　The　experimental　results　show　that　the　SW-CDAN　method　can　effectively　improve　the　classification　performance　and　model　generalization　ability　of　motor　imagery　EEG　signals,　so　that　even　when　facing　a　small　amount　of　motor　imagery　EEG　signals　with　low　effective　components,　it　can　still　maintain　a　high　classification　accuracy.　The　SWCDAN　method　achieves　relatively　high　classification　accuracy　on　BCI　Competition　IV　2a　dataset,　which　is　about　1.87%　higher　than　CDAN　method　respectively.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.