Due　to　the　anonymity　of　blockchain,　frequent　security　incidents　and　attacks　occur　through　it,　among　which　the　Ponzi　scheme　smart　contract　is　a　classic　type　of　fraud　resulting　in　huge　economic　losses.　Machine　learningbased　methods　are　believed　to　be　promising　for　detecting　ethereum　Ponzi　schemes.　However,　there　are　still　some　flaws　in　current　research,　e.g.,　insufficient　feature　extraction　of　Ponzi　scheme　smart　contracts,　without　considering　class　imbalance.　In　addition,　there　is　room　for　improvement　in　detection　precision.　Aiming　at　the　above　problems,　this　paper　proposes　an　ethereum　Ponzi　scheme　detection　scheme　through　opcode　context　analysis　and　adaptive　boosting　(AdaBoost)　algorithm.　Firstly,　this　paper　uses　the　n-gram　algorithm　to　extract　more　comprehensive　contract　opcode　features　and　combine　them　with　contract　account　features,　which　helps　to　improve　the　feature　extraction　effect.　Meanwhile,　adaptive　synthetic　sampling　(ADASYN)　is　introduced　to　deal　with　class　imbalanced　data,　and　integrated　with　the　Adaboost　classifier.　Finally,　this　paper　uses　the　improved　AdaBoost　classifier　for　the　identification　of　Ponzi　scheme　contracts.　Experimentally,　this　paper　tests　our　model　in　real-world　smart　contracts　and　compares　it　with　representative　methods　in　the　aspect　of　F1-score　and　precision.　Moreover,　this　article　compares　and　discusses　the　state　of　art　methods　with　our　method　in　four　aspects:　data　acquisition,　data　preprocessing,　feature　extraction,　and　classifier　design.　Both　experiment　and　discussion　validate　the　effectiveness　of　our　model.　©　2023　CRL　Publishing.　All　rights　reserved.