In　large　group　decision-making　(LGDM),　some　decision-makers　(DMs)　may　engage　in　manipulative　behaviors　driven　by　personal　interests,　while　others　may　become　susceptible　to　manipulation　due　to　the　complexity　and　uncertainty　of　the　decision-making　process.　These　manipulative　and　manipulated　behaviors　hinder　the　effective　achievement　of　group　consensus　and　undermine　the　fairness　and　acceptability　of　the　decision-making　process.　To　address　this,　we　propose　a　two-stage　consensus　model　that　accounts　for　both　manipulative　and　manipulated　behaviors.　First,　the　trust　relationships　among　DMs　are　adjusted　based　on　the　similarity　of　their　evaluations,　and　the　strength　of　these　relationships　is　calculated　using　their　adjusted　mutual　trust　degrees.　Next,　a　clustering　method　based　on　the　fracture　of　relationship　strength　is　introduced　to　classify　DMs　into　subgroups.　By　considering　DMs＇　hesitancy,　trust　relationships,　and　preference　degrees　for　various　alternatives　expressed　in　their　evaluations,　manipulators　are　identified　and　penalized　with　a　weight　penalty.　The　combination　of　hesitation　degree,　trust　degree,　and　similarities　in　alternative　ordinals,　before　and　after　subjective　adjustment,　is　used　to　identify　and　impose　penalties　on　manipulated　DMs.　Furthermore,　various　objective　adjustment　strategies　are　proposed　to　better　manage　the　different　behaviors　of　DMs,　thereby　improving　decision-making　efficiency　and　consensus.　Finally,　an　application　example　and　comparative　analyses　are　presented　to　validate　the　feasibility　of　the　proposed　method.　The　proposed　method　effectively　manages　manipulative　and　manipulated　behaviors,　significantly　enhancing　consensus　efficiency,　fairness,　and　acceptability　in　the　decision-making　process.　©　2025　Elsevier　B.V.