User　preference　prediction　is　a　task　of　learning　user　interests　through　user-item　interactions.　Most　existing　studies　capture　user　interests　based　on　historical　behaviors　without　considering　specific　scenario　information.　However,　the　users　may　have　special　interests　in　these　specific　scenarios　and　sometimes　user　historical　behaviors　are　limited.　In　this　paper,　we　propose　a　Meta-Learned　Specific　Scenario　Interest　Network　(Meta-SSIN)　to　predict　user　preference　of　target　item　by　capturing　specific　scenario　interests.　Meta-SSIN　uses　multiple　independent　meta-learning　modules　to　model　historical　behaviors　in　each　scenario.　The　independent　module　can　capture　special　interests　based　on　limited　behaviors.　Experimental　results　on　three　datasets　show　that　Meta-SSIN　outperforms　compared　state-of-the-art　methods.