With　the　introduction　of　blockchain　technology　and　the　development　of　virtual　currencies,　virtual　currencies,　which　are　based　on　cryptography　to　ensure　the　security　and　anonymity　of　transactions,　have　received　a　lot　of　attention.　As　a　new　payment　method　that　is　secure,　decentralized　and　easy　to　transmit,　virtual　currencies　have　also　attracted　a　large　number　of　illegal　users　and　illegal　transactions.　In　order　to　identify　gambling　transaction　behaviors　and　gambling-related　addresses　in　the　virtual　currency　market,　this　paper　proposes　a　gambling　transaction　feature　extraction　method　based　on　community　detection　and　network　embedding　techniques,　which　obtains　a　network　vector　representation　of　this　transaction　network　structure　by　discovering　a　high　modularity　and　highly　structured　transaction　network　in　gambling　address　transactions　and　performing　node　embedding　and　averaging　calculations　based　on　the　node2vec　algorithm　to　complete　the　extraction　of　transaction　features　of　gambling　addresses　and　solve　the　data　imbalance　problem　of　the　huge　gap　between　the　number　of　historical　transactions　of　different　addresses.　Finally,　based　on　the　feature　dataset　and　several　classical　machine　learning　classification　algorithms,　a　binary　classification　model　is　trained　and　evaluated　to　identify　gambling　transactions　and　addresses.　Experiments　show　that　all　classification　models　achieve　an　accuracy　rate　of　0.72　or　higher　with　high　quality　of　transaction　feature　data,　with　the　lightGBM　model　getting　the　best　result　of　0.84　accuracy　rate,　as　well　as　0.92　and　0.87　recall　and　F1　scores.　©　2024　SPIE.