As　a　senior　function　of　human　brain,　emotion　has　a　great　influence　on　human　study,　work,　and　all　aspects　of　life.　Correctly　recognizing　human　emotion　can　make　artificial　intelligence　serve　human　being　better.　EEG-based　emotion　recognition　(ER)　has　become　more　popular　in　these　years,　which　is　one　of　the　utilizations　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,　based　on　the　time　scale,　we　choose　recurrent　neural　network　as　the　breakthrough　point　of　the　screening　model.　And　according　to　the　rhythmic　characteristics　and　temporal　memory　characteristics　of　EEG,　we　propose　a　Rhythmic　Time　EEG　Emotion　Recognition　Model　(RT-ERM)　based　on　the　valance　and　arousal　of　LSTM.　When　using　this　model,　the　classification　results　of　different　rhythms　and　time　scales　are　different.　Through　the　results　of　the　classification　accuracy　of　different　rhythms　and　different　time　scales,　the　optimal　rhythm　and　time　scale　of　the　RT-ERM　model　are　obtained,　and　the　classification　of　emotional　EEG　is　carried　out　by　the　best　time　scales　corresponding　to　different　rhythms,　and　we　found　some　interesting　phenomena.　Finally,　by　comparing　with　other　existing　emotional　EEG　classification　methods,　it　is　found　that　the　rhythm　and　time　scale　of　the　model　can　provide　a　good　accuracy　rate　for　RT-ERM.　©　2018,　Springer　Nature　Switzerland　AG.