Nowadays,　the　Internet　of　Things　(IoT)　has　developed　rapidly.　To　deal　with　the　security　problems　in　some　of　the　IoT　applications,　blockchain　has　aroused　lots　of　attention　in　both　academia　and　industry.　In　this　paper,　we　consider　the　mobile　blockchain　supporting　IoT　applications,　and　the　mobile　edge　computing　(MEC)　is　deployed　at　the　Small-cell　Base　Station　(SBS)　as　a　supplement　to　enhance　the　computation　ability　of　IoT　devices.　To　encourage　the　participation　of　the　SBS　in　the　mobile　blockchain　networks,　the　long-term　revenue　of　the　SBS　is　considered.　The　task　scheduling　problem　maximizing　the　long-term　mining　reward　and　minimizing　the　resource　cost　of　the　SBS　is　formulated　as　a　Markov　Decision　Process　(MDP).　To　achieve　an　efficient　intelligent　strategy,　the　deep　reinforcement　learning　(DRL)　based　solution　named　policy　gradient　based　computing　tasks　scheduling　(PG-CTS)　algorithm　is　proposed.　The　policy　mapping　from　the　system　state　to　the　task　scheduling　decision　is　represented　by　a　deep　neural　network.　The　episodic　simulations　are　built　and　the　REINFORCE　algorithm　with　baseline　is　used　to　train　the　policy　network.　According　to　the　training　results,　the　PG-CTS　method　is　about　10%　better　than　the　second-best　method　greedy.　The　generalization　ability　of　PG-CTS　is　proved　theoretically,　and　the　testing　results　also　show　that　the　PG-CTS　method　has　better　performance　over　the　other　three　strategies,　greedy,　first-in-first-out　(FIFO)　and　random　in　different　environments.