<p>With the swift advancement of social technology, the problem of Large-Scale Group Decision Making (GDM) has emerged as a new research focus. Nevertheless, upon conducting an in-depth exploration, it is discerned that this field is beset with many challenges. For instance, conventional clustering techniques struggle to cope with the complex structures of large-scale data, and group manipulation behavior in the feedback adjustment stage can seriously disrupt the fairness of the decision-making process. Therefore, this paper advances a large-scale multi-attribute GDM method that integrates the Gaussian mixture model (GMM) and is efficacious in preventing group manipulation. Firstly, the GMM is incorporated into the clustering of probabilistic linguistic term sets. Subsequently, the probability information gleaned from the clustering outcomes is utilized to generate the weights of decision-makers, which are then aggregated to derive the comprehensive perspectives of the distinct subgroups. Thereafter, the discrepant subgroup opinions that surface during the consensus measurement are subjected to adjustment. Considering the group manipulation issue that may arise during this adjustment, we establish an optimal feedback model with the minimization of adjustment cost and the maximization of fairness level to prevent group manipulation. Ultimately, the effectiveness and superiority of the proposed approach are affirmed by a low-altitude economy numerical analysis.</p>