In　Mobile　Crowdsensing　(MCS)　systems,　cloud　service　providers　(CSPs)　pay　for　and　analyze　the　sensing　data　collected　by　mobile　devices　(MDs)　to　enhance　the　Quality-of-Service　(QoS).　Therefore,　it　is　necessary　to　guarantee　security　when　CSPs　and　users　conduct　transactions.　Blockchain　can　secure　transactions　between　two　parties　by　using　the　Proof-of-Work　(PoW)　to　confirm　transactions　and　add　new　blocks　to　the　chain.　Nevertheless,　the　complex　PoW　seriously　hinders　applying　Blockchain　into　MCS　since　MDs　are　equipped　with　limited　resources.　To　address　these　challenges,　we　first　design　a　new　consortium　blockchain　framework　for　MCS,　aiming　to　assure　high　reliability　in　complex　environments,　where　a　novel　Credit-based　Proof-of-Work　(C-PoW)　algorithm　is　developed　to　relieve　the　complexity　of　PoW　while　keeping　the　reliability　of　blockchain.　Next,　we　propose　a　new　scalable　Deep　Reinforcement　learning　based　Computation　Offloading　(DRCO)　method　to　handle　the　computation-intensive　tasks　of　C-PoW.　By　combining　Proximal　Policy　Optimization　(PPO)　and　Differentiable　Neural　Computer　(DNC),　the　DRCO　can　efficiently　make　the　optimal/near-optimal　offloading　decisions　for　C-PoW　tasks　in　blockchain-enabled　MCS　systems.　Extensive　experiments　demonstrate　that　the　DRCO　reaches　a　lower　total　cost　(weighted　sum　of　latency　and　power　consumption)　than　state-of-the-art　methods　under　various　scenarios.