In　consensus-based　large　scale　group　decision　making　(LSGDM)　problems,　some　experts　often　exhibit　adverse　selection　behavior　due　to　asymmetry　in　information　availability.　This　may　lead　to　results　deviating　from　the　optimum,　weakening　decision　making　fairness　and　reducing　consensus　efficiency.　For　this　reason,　this　paper　proposes　a　large　group　consensus　decision　making　method　based　on　managing　adverse　selection　behavior　in　an　asymmetric　information　environment.　Firstly,　the　directed　Louvain　algorithm　is　introduced　to　achieve　the　decision　making　subgroup　division　based　on　the　directed　social　network.　On　this　basis,　considering　the　different　qualifications　and　research　fields　of　experts,　a　new　weight　allocation　method　is　proposed　based　on　the　authority　of　experts.　Next,　focusing　on　the　consensus-reaching　process,　a　mechanism　for　identifying　and　managing　adverse　selection　behaviors　is　proposed.　A　hierarchical　recognition　framework　is　designed　for　behavior　identification,　incorporating　behavioral　patterns　and　underlying　motivations.　A　multidimensional　dynamic　adjustment　strategy　based　on　weight　and　preference　is　introduced　for　behavior　management,　then　a　comprehensive　large-group　consensus　decision　making　method　based　on　adverse　selection　behavior　management　is　developed.　Finally,　the　feasibility　and　effectiveness　of　the　proposed　method　are　verified　using　case　studies　and　parameter　discussions.　©　2025　Elsevier　Ltd