Deep　learning　is　known　to　be　effective　at　automating　the　generation　of　representations,　which　has　achieved　great　success　by　learning　efficient　representations　from　data,　especially　for　user　and　item　representations　in　sequential　recommendation.　However,　most　existing　methods　usually　represent　a　user＇s　interest　by　one　independent　representation　vector,　which　is　inadequate　to　capture　user＇s　diverse　interests.　We　aim　to　fully　characterize　the　diversity　of　user　interests　in　this　study　to　improve　the　recommendation　performance.　We　propose　a　Multiple　User　Interest　Representations　(MUIR)　model　that　learns　and　fuses　user＇s　interests　from　different　aspects.　To　learn　different　levels　of　user　interests,　we　specifically　leverage　two　self-attention-based　modules　that　better　capture　user＇s　local　and　global　interests　respectively.　By　considering　the　information　of　the　recent　interacted　items,　we　further　design　a　gating　module　to　balance　the　interests,　which　is　capable　of　modeling　how　user　interests　evolve　and　interact　in　recent　behavior　sequence.　Extensive　experiments　conducted　on　the　real-world　datasets　demonstrate　that　our　model,　MUIR,　outperforms　existing　state　of-the-art　methods　significantly.