PurposeIn　large-scale　group　decision-making　(LSGDM)　situations,　existing　TODIM　group　decision-making　methods　often　fail　to　account　for　the　influence　of　social　network　relationships　and　the　bounded　rationality　of　decision-makers　(DMs).　To　address　this　issue,　a　new　TODIM-based　group　decision-making　method　is　proposed　that　considers　the　current　trust　relationships　among　DMs　in　a　large-scale　trust　relationship　network.Design/methodology/approachThis　method　consists　of　two　main　stages.　In　the　first　stage,　the　large-scale　group　is　partitioned　into　several　sub-clusters　based　on　trust　relationships　among　DMs.　The　dominance　degree　matrix　of　each　sub-cluster　is　then　aggregated　into　the　large-scale　group　dominance　degree.　In　the　second　stage,　after　aggregating　the　large-scale　group　dominance　degree,　the　consensus　index　is　calculated　to　identify　any　inconsistent　sub-clusters.　Feedback　adjustments　are　made　based　on　trust　relationships　until　a　consensus　is　reached.　The　TODIM　method　is　then　applied　to　calculate　the　corresponding　ranking　results.　Finally,　an　illustrative　example　is　applied　to　show　the　feasibility　of　the　proposed　model.FindingsThe　proposed　method　is　practical　and　effective　which　is　verified　by　the　real　case　study.　By　taking　into　account　the　trust　relationships　among　DMs　in　the　core　process　of　LSGDM,　it　indeed　has　an　impact　on　the　decision　outcomes.　We　also　specifically　address　this　issue　in　Chapter　Five.　The　proposed　method　fully　incorporates　the　bounded　rationality　of　DMs,　namely　their　tendency　to　accept　the　opinions　of　trusted　experts,　which　aligns　more　with　their　psychology.　The　two-stage　consensus　model　proposed　in　this　paper　effectively　addresses　the　limitations　of　traditional　assessment-based　methods.Originality/valueThis　study　establishes　a　two-stage　consensus　model　based　on　trust　relationships　among　DMs,　which　can　assist　DMs　in　better　understanding　trust　issues　in　complex　decision-making,　enhancing　the　accuracy　and　efficiency　of　decisions,　and　providing　more　scientific　decision　support　for　organizations　such　as　businesses　and　governments.