The　consensus　reaching　process　(CRP)　represents　a　multi-round　dynamic　method　essential　for　harmonizing　the　interests　of　multiple　parties.　With　the　rise　of　instant　messaging　and　social　media,　the　complexity　of　individual　social　trust　networks　and　structures.　Therefore,　it　is　crucial　to　explore　the　inherent　value　of　trust　networks　in　the　context　of　multi-person　multi-criteria　large-scale　decision-making　(MpMcLSDM)　to　facilitate　consensus.　This　paper　develops　a　minimum　adjustment　consensus　model　(MACM)　for　MpMcLSDM　based　on　social　trust　network　analysis　(STNA).　First,　the　consistency　path　rule　and　personal　traits　are　defined　through　STNA,　leading　to　a　formulated　strategy　for　the　completion　of　the　trust　relationship.　Subsequently,　a　novel　centrality　measure,　informed　by　the　consistency　path　rule,　is　proposed,　and　a　weight　method　is　devised　to　determine　decision-maker　(DM)　weights　and　sub-cluster　weights　after　clustering.　This　paper　further　elucidates　the　implications　of　consensus　level　fluctuations　on　DM　self-confidence　and　opinion　inclination.　Ultimately,　a　MACM　is　constructed　within　the　MpMcLSDM　framework,　integrating　opinion　dynamics.　A　numerical　example　demonstrates　the　model＇s　effectiveness,　and　comparisons　with　other　methods　show　its　rationale　and　improvement　in　performance.