In　recent　years,　the　decentralization,　anonymity,　and　cross-border　capabilities　of　cryptocurrencies　have　significantly　increased　their　use　in　money　laundering　activities.　In　an　era　rigorously　regulated　by　enhanced　global　anti-money　laundering　(AML)　measures,　designing　an　efficient　approach　to　detect　potential　money　laundering　in　blockchain　is　essential.　In　this　research,　we　present　GraphALM,　an　active　learning　model　based　on　reinforcement　learning,　aiming　to　improve　the　detection　performance　of　money　laundering　activities　in　blockchain　transactions.　This　model　addresses　the　challenge　of　efficiently　sampling　training　data　batches　to　identify　illicit　activities　within　the　vast　and　complex　dataset　of　Bitcoin　transactions.　Additionally,　we　have　constructed　a　new　Realistic　and　Demand　(RD)　Bitcoin　dataset,　augmented　with　feature　uncertainty,　to　better　simulate　real-world　scenarios.　The　results　of　our　experiments　demonstrate　the　effectiveness,　robustness,　and　explainability　of　our　proposed　model,　contributing　to　the　application　of　active　learning　strategies　in　the　field　of　financial　regulation　within　blockchain　networks.　　©　2024　IEEE.