Sequential　models　based　on　deep　learning　are　widely　used　in　sequential　recommendation　task,　but　the　increase　of　model　parameters　results　in　a　higher　latency　in　the　inference　stage,　which　limits　the　real-time　performance　of　the　model.　In　order　to　make　the　model　strike　a　balance　between　efficiency　and　effectiveness,　the　knowledge　distillation　technology　is　adopted　to　transfer　the　pre-trained　knowledge　from　the　large　teacher　model　to　the　small　student　model.　We　propose　a　multi-stage　knowledge　distillation　method　based　on　interest　knowledge,　including　interest　representation　knowledge　and　interest　drift　knowledge.　In　the　process　of　knowledge　transfer,　expert　distillation　is　designed　to　transform　the　knowledge　dimension　of　student　model　to　alleviate　the　loss　of　original　knowledge　information.　Specially,　curriculum　learning　is　introduced　for　multistage　knowledge　learning,　which　further　makes　the　teacher　model　effectively　transfer　the　knowledge　to　the　student　model　with　limited　ability.　The　proposed　method　on　three　real-world　datasets　including　MovieLen-1M,　Amazon　Game　and　Steam　datasets.　The　experimental　results　demonstrate　that　our　method　is　superior　to　the　other　compared　distillation　method　significantly　and　multi-stage　learning　makes　the　student　model　achieve　the　knowledge　step　by　step　for　improvement.