Large-scale　group　decision　making　(LSGDM)　is　increasingly　prevalent　in　practical　scenarios,　with　consensus　reaching　being　a　crucial　aspect　that　concerns　the　effectiveness　and　efficiency　of　the　decision-making　process.　This　paper　proposes　an　innovative　consensus　reaching　method　for　LSGDM,　adopting　a　novel　perspective　that　focuses　on　gains　and　losses.　First,　the　gains　and　losses　of　experts　during　the　clustering　process　are　computed　using　recognition　increment　and　representativeness　decrement,　which　are　combined　to　determine　their　utility.　By　ensuring　that　experts　receive　a　high　level　of　utility,　a　clustering　method　is　proposed　to　categorize　a　large　number　of　experts　into　distinct　clusters.　Then,　an　optimization　model　is　presented　to　determine　the　weights　of　clusters,　with　the　objective　of　maximizing　the　utility　of　clusters.　Next,　a　feedback　mechanism　is　developed,　grounded　in　the　concept　of　gains　and　losses,　to　enhance　consensus　levels.　During　the　feedback　adjustment　process,　the　gains　and　losses　of　clusters　are　assessed　based　on　changes　in　consensus　levels　and　the　adjustment　costs　incurred　when　clusters　modify　their　information.　These　gains　and　losses　are　combined　to　determine　the　utility　of　clusters,　serving　as　the　foundation　for　designing　the　feedback　mechanism.　Finally,　an　application　example　of　blockchain　platform　selection　is　presented,　along　with　comparative　analyses　to　validate　the　proposed　method.　©　2025　Elsevier　Ltd