Driven　by　numerous　emerging　mobile　devices　and　various　quality　of　service　requirements,　mobile　edge　computing　(MEC)　has　been　recognized　as　a　prospective　paradigm　to　promote　the　computation　capability　of　mobile　devices,　as　well　as　reduce　energy　overhead　and　service　latency　of　applications　for　the　Internet　of　Things　(IoT).　However,　there　are　still　some　open　issues　in　the　existing　research　works:　1)　limited　network　and　computing　resource,　2)　simple　or　non-intelligent　resource　management,　3)　ignored　security　and　reliability.　In　order　to　cope　with　these　issues,　in　this　article,　6G　and　blockchain　technology　are　considered　to　improve　network　performance　and　ensure　the　authenticity　of　data　sharing　for　the　MEC-enabled　IoT.　Meanwhile,　a　novel　intelligent　optimization　method　named　as　collective　reinforcement　learning　(CRL)　is　proposed　and　introduced,　to　realize　intelligent　resource　allocation,　meet　distributed　training　results　sharing　and　avoid　excessive　consumption　of　system　resources.　Based　on　the　designed　network　model,　a　cloud-edge　collaborative　resource　allocation　framework　is　formulated.　By　joint　optimizing　the　offloading　decision,　block　interval　and　transmission　power,　it　aims　to　minimize　the　consumption　overheads　of　system　energy　and　service　latency.　Then　the　formulated　problem　is　designed　as　a　Markov　decision　process,　and　the　optimal　strategy　can　be　obtained　by　the　CRL.　Some　evaluation　results　reveal　that　the　system　performance　based　on　the　proposed　scheme　outperforms　other　existing　schemes　obviously.