In large-scale group decision-making, obtaining reasonable and reliable decision results is the research focus. To this end, a dual-level consensus model for large-scale group decision-making is constructed. In the model, we first presented a method for measuring the two-dimensional similarity between decision-makers. The method considered the opinion similarity at evaluation scales and ranking results level, which provided a more comprehensive measure of the opinion differences between decision-makers. A hierarchical clustering algorithm based on the two-dimensional similarity-trust matrix is proposed. By considering the two-dimensional similarity and trust relationships among decision-makers, the method contributed to the formation of subgroups with excellent cooperative and communicative atmospheres. Furthermore, we introduced a dual-level consensus measures method to comprehensively and accurately assess the group consensus level. To increase the efficiency of decision-making and the satisfaction of decision-makers, we proposed a personalized feedback adjustment mechanism for decision-makers. Additionally, for the subgroups that have not reached consensus, a social networks DeGroot model is constructed to adjust the opinion of subgroups. Finally, a case study of low-carbon supplier selection verified the feasibility and applicability of the model. The effectiveness and superiority of the model are demonstrated by comparative and simulation analysis. The analysis results show that the model is conducive to promoting cooperation and coordination among decision-makers and obtaining highly acceptable and quality decision results.