Immutability,　decentralization,　and　linear　promoted　scalability　make　sharded　blockchain　a　promising　solution,　which　can　effectively　address　the　trust　issue　in　the　large-scale　Internet　of　Things　(IoT).　However,　currently,　the　throughput　of　sharded　blockchains　is　still　limited　when　it　comes　to　high　proportions　of　cross-shard　transactions　(CST).　On　the　other　hand,　assemblage　characteristics　of　collaborative　computing　in　IoT　have　not　been　received　attention.　Therefore,　in　this　paper,　we　present　a　clustering-based　sharded　blockchain　strategy　for　collaborative　computing　in　the　IoT,　where　the　sharding　of　the　blockchain　system　is　implemented　in　two　steps:　k-means　clustering-based　user　grouping　and　the　assignment　of　consensus　nodes.　In　this　framework,　how　to　reasonably　group　the　IoT　users　while　simultaneously　guaranteeing　the　system　performance　is　the　key　point.　Specifically,　we　describe　the　data　transactions　among　IoT　devices　by　data　transaction　flow　graph　(DTFG)　based　on　a　dynamic　stochastic　block　model.　Then,　formed　as　a　Markov　decision　process　(MDP),　the　optimization　of　the　cluster　number　(shard　number)　and　the　adjustment　of　consensus　parameters　are　jointly　trained　by　deep　reinforcement　learning　(DRL).　Simulation　results　show　that　the　proposed　scheme　improves　the　scalability　of　the　sharded　blockchain　in　the　IoT　application.