Industrial　Internet　of　Things　(IIoT)　has　emerged　with　the　developments　of　various　communication　technologies.　In　order　to　guarantee　the　security　and　privacy　of　massive　IIoT　data,　blockchain　is　widely　considered　as　a　promising　technology　and　applied　into　IIoT.　However,　there　are　still　several　issues　in　the　existing　blockchain-enabled　IIoT:　1)　unbearable　energy　consumption　for　computation　tasks;　2)　poor　efficiency　of　consensus　mechanism　in　blockchain;　and　3)　serious　computation　overhead　of　network　systems.　To　handle　the　above　issues　and　challenges,　in　this　article,　we　integrate　mobile-edge　computing　(MEC)　into　blockchain-enabled　IIoT　systems　to　promote　the　computation　capability　of　IIoT　devices　and　improve　the　efficiency　of　the　consensus　process.　Meanwhile,　the　weighted　system　cost,　including　the　energy　consumption　and　the　computation　overhead,　are　jointly　considered.　Moreover,　we　propose　an　optimization　framework　for　blockchain-enabled　IIoT　systems　to　decrease　consumption,　and　formulate　the　proposed　problem　as　a　Markov　decision　process　(MDP).　The　master　controller,　offloading　decision,　block　size,　and　computing　server　can　be　dynamically　selected　and　adjusted　to　optimize　the　devices　energy　allocation　and　reduce　the　weighted　system　cost.　Accordingly,　due　to　the　high-dynamic　and　large-dimensional　characteristics,　deep　reinforcement　learning　(DRL)　is　introduced　to　solve　the　formulated　problem.　Simulation　results　demonstrate　that　our　proposed　scheme　can　improve　system　performance　significantly　compared　to　other　existing　schemes.　©　2014　IEEE.