The　advent　of　cryptocurrency　introduced　by　Bitcoin　ignited　an　explosion　of　technological　and　entrepreneurial　interest　in　payment　processing.　The　user　scale　of　Bitcoin　is　dynamic,　and　the　participating　identities　are　anonymous,　which　will　lead　to　more　hidden,　sophisticated　and　intelligent　money　laundering　crimes.　Therefore,　in　order　to　realize　intelligent　anti-money　laundering,　it　is　necessary　to　accurately　detect　abnormal　transactions.　Recently,　graph　representation　learning　has　shown　strong　advantages　in　the　field　of　machine　learning,　and　the　current　blockchain　anomaly　detection　models　based　on　graph　representation　learning　are　mainly　designed　for　static　graphs,　however,　real-world　graphs　evolve　over　time.　Based　on　this,　this　paper　proposes　a　block-chain　abnormal　transaction　detection　model　DynAEGCN　based　on　dynamic　graph　representation　learning.　This　model　uses　the　autoencoder　as　the　framework.　Firstly,　the　encoder　uses　the　graph　convolutional　neural　networks　to　gather　neighborhood　information　to　obtain　lowdimensional　feature　vectors.　Then,　considering　the　dynamics　of　graphs,　the　GRU　network　is　used　to　evolve　the　graph　model　itself　over　time.　Finally,　the　decoder　reconstructs　the　adjacency　matrix　and　compares　it　with　the　real　graph　to　construct　the　loss.　Extensive　experiments　on　the　Bitcoin　transaction　dataset　for　edge　classification　tasks　against　financial　crimes　show　that　DynAEGCN　model　has　better　performance　compared　with　related　approaches.