The　topic　of　consensus　cost　spent　in　the　large　scale　group　consensus　decision　making　(LSGDM)　process　has　drawn　more　attention　as　decision-making　environments　have　grown　more　complicated.　Meanwhile,　the　social　network　is　an　important　element　in　LSGDM　that　can　be　used　to　promote　group　consensus　by　social　relationships　among　decision　makers.　Decision　makers　can　be　classified　into　opinion　leader　and　opinion　followers,　whose　opinion　evolution　show　different　results.　Therefore,　taking　the　social　relations　of　group　as　an　entry　point　for　the　LSGDM　problem,　this　paper　proposes　a　minimum　cost　two-stage　consensus　decision　model　based　on　the　opinion　leader-follower　opinion　evolution　for　the　LSGDM　problem,　and　extends　it　to　the　distributed　linguistic　environment.　First,　the　sub-network　partitioning　method　is　used　to　divide　the　large　scale　group　into　several　sub-clusters,　and　then　the　improved　PageRank　algorithm　is　used　to　calculate　the　weights　of　decision　makers.　Next,　the　consensus　reaching　process　(CRP)　is　divided　into　two　stages.　In　the　first　stage,　an　opinion　evolution　model　based　on　opinion　leader-follower　is　designed　on　the　different　opinion　evolution　processes　of　opinion　leader　and　opinion　follower.　On　this　basis,　we　propose　an　intra-sub-cluster　minimum　cost　consensus　model　based　on　leader-follower　opinion　dynamics.　The　adjustment　suggestions　are　obtained　by　minimizing　the　adjustment　cost　of　opinions　to　reach　consensus　within　the　sub-clusters.　In　the　second　stage,　we　use　the　inter-sub-cluster　minimum　cost　consensus　model　based　on　global　coordination　to　lead　the　sub-clusters　to　reach　the　collective　consensus　with　minimum　opinion　adjustment　cost.　Finally,　a　case　study　on　power　engineering　projects　illustrates　the　feasibility　of　the　model,　and　comparing　with　other　models　reveals　its　advantages.　In　general,　the　two-stage　consensus　reaching　model　we　proposed　can　effectively　use　the　leadership　role　of　opinion　leaders　to　facilitate　the　consensus　building　process　and　reduce　consensus　costs.