Recently　there　has　attracted　wide　attention　in　EEG-based　emotion　recognition　(ER),　which　is　one　of　the　utilization　of　Brain　Computer　Interface　(BCI).　However,　due　to　the　ambiguity　of　human　emotions　and　the　complexity　of　EEG　signals,　the　EEG-ER　system　which　can　recognize　emotions　with　high　accuracy　is　not　easy　to　achieve.　In　this　paper,　by　combining　discrete　wavelet　transform,　correlation　analysis,　and　neural　network　methods,　we　propose　an　Emotional　Recognition　model　based　on　rhythm　synchronization　patterns　to　distinguish　the　emotional　stimulus　responses　to　different　emotional　audio　and　video.　In　this　model,　the　entire　scalp　conductance　signal　is　analyzed　from　a　joint　time-frequency-space　correlation,　which　is　beneficial　to　the　depth　learning　and　expression　of　affective　pattern,　and　then　improve　the　accuracy　of　recognition.　The　accuracy　of　the　proposed　multi-layer　EEG-ER　system　is　compared　with　various　feature　extraction　methods.　For　analysis　results,　average　and　maximum　classification　rates　of　64%　and　67.0%　were　obtained　for　arousal　and　66.6%　and　76.0%　for　valence.