As　an　advanced　function　of　the　human　brain,　emotion　has　a　significant　influence　on　human　studies,　works,　and　other　aspects　of　life.　Artificial　Intelligence　has　played　an　important　role　in　recognizing　human　emotion　correctly.　EEG-based　emotion　recognition　(ER),　one　application　of　Brain　Computer　Interface　(BCI),　is　becoming　more　popular　in　recent　years.　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.　Based　on　the　time　scale,　this　paper　chooses　the　recurrent　neural　network　as　the　breakthrough　point　of　the　screening　model.　According　to　the　rhythmic　characteristics　and　temporal　memory　characteristics　of　EEG,　this　research　proposes　a　Rhythmic　Time　EEG　Emotion　Recognition　Model　(RT-ERM)　based　on　the　valence　and　arousal　of　Long–Short-Term　Memory　Network　(LSTM).　By　applying　this　model,　the　classification　results　of　different　rhythms　and　time　scales　are　different.　The　optimal　rhythm　and　time　scale　of　the　RT-ERM　model　are　obtained　through　the　results　of　the　classification　accuracy　of　different　rhythms　and　different　time　scales.　Then,　the　classification　of　emotional　EEG　is　carried　out　by　the　best　time　scales　corresponding　to　different　rhythms.　Finally,　by　comparing　with　other　existing　emotional　EEG　classification　methods,　it　is　found　that　the　rhythm　and　time　scale　of　the　model　can　contribute　to　the　accuracy　of　RT-ERM.　©　2019,　The　Author(s).