The　aim　of　this　study　is　to　recognize　human　emotions　by　electroencephalographic　(EEG)　signals.　The　innovation　of　our　research　methods　involves　two　aspects:　First,　we　integrate　the　spatial　characteristics,　frequency　domain,　and　temporal　characteristics　of　the　EEG　signals,　and　map　them　to　a　two-dimensional　image.　With　these　images,　we　build　a　series　of　EEG　Multidimensional　Feature　Image　(EEG　MFI)　sequences　to　represent　the　emotion　variation　with　EEG　signals.　Second,　we　construct　a　hybrid　deep　neural　network　to　deal　with　the　EEG　MFI　sequences　to　recognize　human　emotional　states　where　the　hybrid　deep　neural　network　combined　the　Convolution　Neural　Networks　(CNN)　and　Long　Short-Term-Memory　(LSTM)　Recurrent　Neural　Networks　(RNN).　Empirical　research　is　carried　out　with　the　open-source　dataset　DEAP　(a　Dataset　for　Emotion　Analysis　using　EEG,　Physiological,　and　video　signals)　using　our　method,　and　the　results　demonstrate　the　significant　improvements　over　current　state-of-the-art　approaches　in　this　field.　The　average　emotion　classification　accuracy　of　each　subject　with　CLRNN　(the　hybrid　neural　networks　that　we　proposed　in　this　study)　is　75.21%.