<p>Large-scale group decision-making problems based on social network analysis have garnered significant attention in recent years. Yet, few studies have addressed the challenge of expert heterogeneity in such decision-making. So, this paper constructs a large-scale group decision-making model based on a local search algorithm and heterogeneous feedback adjustment. Specifically, we employ a novel community detection method, the local search algorithm, to mine experts’ local information and identify community structures, constructing a multi-layer network model to clearly characterize the dynamic interaction relationships among experts. Building upon this foundation, we introduce a multi-attribute bounded confidence model. By integrating social network analysis metrics such as degree centrality and proximity centrality, we define both the global and local weights of experts. Additionally, to address the role differences between leaders and followers within the community, a consensus-reaching process with heterogeneous feedback mechanisms was designed; leaders focus on opinion steering, while followers concentrate on consensus building, thereby enhancing decision-making efficiency. In simulation analysis, we demonstrated that incorporating leader guidance, specifically, designing a heterogeneous feedback adjustment mechanism, can reduce the number of iterations required to reach consensus. Furthermore, as the leader’s weight increases, the number of iterations decreases. Finally, case studies and comparative experiments validated the effectiveness and practicality of the proposed method.</p>