Sleep　staging　is　one　of　the　important　methods　to　diagnosis　and　treatment　of　sleep　diseases.　However,　it　is　laborious　and　time-consuming,　therefore,　computer　assisted　sleep　staging　is　necessary.　Most　of　the　existing　sleep　staging　researches　using　hand-engineered　features　rely　on　prior　knowledges　of　sleep　analysis,　and　usually　single　channel　electroencephalogram　(EEG)　is　used　for　sleep　staging　task.　Prior　knowledge　is　not　always　available,　and　single　channel　EEG　signal　cannot　fully　represent　the　patient＇s　sleeping　physiological　states.　To　tackle　the　above　two　problems,　we　propose　an　automatic　sleep　staging　network　model　based　on　data　adaptation　and　multimodal　feature　fusion　using　EEG　and　electrooculogram　(EOG)　signals.　3D-CNN　is　used　to　extract　the　time-frequency　features　of　EEG　at　different　time　scales,　and　LSTM　is　used　to　learn　the　frequency　evolution　of　EOG.　The　nonlinear　relationship　between　the　High-layer　features　of　EEG　and　EOG　is　fitted　by　deep　probabilistic　network.　Experiments　on　SLEEP-EDF　and　a　private　dataset　show　that　the　proposed　model　achieves　state-of-the-art　performance.　Moreover,　the　prediction　result　is　in　accordance　with　that　from　the　expert　diagnosis.