Multicriterion group classification decision‐making (MCGCDM) involves evaluating alternatives under a specific set of criteria and assigning them to a predetermined set of ordinal categories. One of the main challenges of current research in MCGCDM is establishing a more comprehensive and dynamic feedback adjustment mechanism in the consensus‐reaching process, which ensures the objectivity of the output while better preserving the true intentions of the decision‐makers (DMs). Therefore, this paper constructs a consensus‐driven MCGCDM method based on dynamic trust interaction and overlapping communities’ perspectives. First, the latent factor model algorithm is introduced into the social trust network (STN) based on overlapping community detection to receive the community relations between DMs. Second, according to the trust value and community connection of DMs in STN, a comprehensive, objective, and dynamic experts’ weights calculation method is proposed. Besides, a trust evolution method relying on classification similarity is developed, achieving dynamic updates of trust networks and overlapping communities. A personalized consensus optimization guided by an adaptive adjustment mechanism is designed, emphasizing the significance of overlapping DMs and group opinions while strictly adhering to minimal adjustment. Subsequently, the green building rating is used as an explanatory case to prove the feasibility and objectivity of this method. Eventually, through a series of comparative analyses, the effectiveness and advantages of the proposed method are verified.

The q-rung orthopair fuzzy sets (q-ROFs) are an important way to express uncertain information, and they are superior to the intuitionistic fuzzy sets and the Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we propose the q-rung orthopair fuzzy weighted averaging operator and the q-rung orthopair fuzzy weighted geometric operator to deal with the decision information, and their some properties are well proved. Further, based on these operators, we presented two new methods to deal with the multi-attribute decision making problems under the fuzzy environment. Finally, we used some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.