The　rise　of　edge　intelligence　is　driving　a　shift　in　the　focus　of　complexity　computing　to　the　edge.　Due　to　network　and　communication　constraints,　traditional　edge　computing　resource　scheduling　solutions　for　industrial　Internet　of　Thing　(IIoT)　usually　face　many　challenges.　For　example,　delayed　decision　release,　unreasonable　policy　scheduling　and　under-utilization　of　resources.　These　problems　hinder　the　further　construction　and　advancement　of　intelligent　IIoT.　In　order　to　solve　these　problems,　this　paper　proposes　an　edge　computing　resource　scheduling　scheme　based　on　collective　learning.　The　process　of　model　training　is　formulated　as　a　Markovian　decision　process　(MDP).　The　scheme　enables　edge　nodes　to　exchange　learning　experiences　of　resource　scheduling　schemes,　through　a　shared　ledger　on　the　blockchain,　including　parameters　for　initial　model　training.　The　updated　policy　scheduling　scheme　is　then　obtained　through　a　collective　deep　reinforcement　learning　(CDRL)　algorithm.　Also,　to　reduce　the　transmission　burden　of　the　underlying　industrial　devices,　we　benefit　ambient　backscatter　communication　(AmBC)　to　improve　the　power　utilization　of　battery.　Simulation　results　display　our　proposed　scheme　can　reduce　energy　consumption　significantly,　while　decreased　approximately　12.6%　compare　to　A3C　algorithm.