With　the　increased　emphasis　on　data　security　in　the　Internet　of　Things　(IoT),　blockchain　has　received　more　and　more　attention.　Due　to　the　computing　consuming　characteristics　of　blockchain,　mobile　edge　computing　(MEC)　is　integrated　into　IoT.　However,　how　to　efficiently　use　edge　computing　resources　to　process　the　computing　tasks　of　blockchain　from　IoT　devices　has　not　been　fully　studied.　In　this　paper,　the　MEC　and　blockchain-enhanced　IoT　is　considered.　The　transactions　recording　the　data　or　other　application　information　are　generated　by　the　IoT　devices,　and　they　are　offloaded　to　the　MEC　servers　to　join　the　blockchain.　The　practical　Byzantine　fault　tolerance　(PBFT)　consensus　mechanism　is　used　among　all　the　MEC　servers　which　are　also　the　blockchain　nodes,　and　the　latency　of　the　consensus　process　is　modeled　with　the　consideration　of　characteristics　of　the　wireless　network.　The　joint　optimization　problem　of　serving　base　station　(BS)　selection　and　wireless　transmission　resources　allocation　is　modeled　as　a　Markov　decision　process　(MDP),　and　the　long-term　system　utility　is　defined　based　on　task　reward,　credit　value,　the　latency　of　infrastructure　layer　and　blockchain　layer,　and　computing　cost.　A　double　deep　Q　learning　(DQN)　based　transactions　offloading　algorithm　(DDQN-TOA)　is　proposed,　and　simulation　results　show　the　advantages　of　the　proposed　algorithm　in　comparison　to　other　methods.　©　2023　Inst.　of　Scientific　and　Technical　Information　of　China.　All　rights　reserved.