Large-scale　group　decision　making　(LSGDM)　is　considered　when　the　number　of　experts　involved　in　the　decision　exceeds　20.　Due　to　the　large　number　of　people　involved　in　LSGDM,　its　decision-making　process　is　uncertain,　complex,　and　time-consuming.　Therefore,　how　to　effectively　help　large　groups　reach　consensus　in　a　complex　environment　is　a　challenge　for　current　research.　Consensus　reaching　process　(CRP)　is　an　effective　tool　to　eliminate　group　conflicts.　Based　on　this,　we　propose　a　consensus　reaching　process　based　on　the　Louvain　algorithm,　social　network,　and　bounded　confidence　(SNBC)　model　with　interval　numbers.　First,　we　use　interval　numbers　to　express　expert　opinions　and　social　network　relationships　among　experts.　Second,　the　experts　are　clustered　using　the　Louvain　algorithm.　The　weights　of　experts　are　obtained　by　social　network　analysis.　Third,　we　use　the　SNBC　model　to　design　a　feedback　mechanism　for　tripartite　opinions.　In　addition,　we　give　a　numerical　example　and　simulation　experiments　to　demonstrate　the　flexibility　and　effectiveness　of　the　proposed　approach.　Finally,　the　comparative　analysis　shows　the　superiority　of　our　method.