To address the fuzziness of information and the dynamic interference among trust paths in the uncertain social network group decision-making (SN-GDM) environment, this article evaluates information based on the linguistic Z-numbers (LZNs) representation and proposes a fuzzy group decision-making model considering quantum interference effects. First, the model updates trust relationships through a dynamic trust transfer process and incorporates quantum interference among trust paths to compute the comprehensive trust degree of each decision-maker (DM), which is then transformed into the corresponding DMs’ weights. The interference angle is measured by the quantum gate transform and quantum Fourier transform (QFT). Second, an optimization model is constructed by integrating the distance relationship between DMs’ psychological pReferences and evaluation information to determine the optimal attributes’ weights. On this basis, the scores of the alternatives are calculated and ranked. Finally, a case study on emergency response is conducted to verify the feasibility and effectiveness of the proposed model, followed by a sensitivity analysis of the rotation angle parameter and the information transmission discount factor, as well as a comparative analysis with existing decision-making models. The results demonstrate that the proposed model not only possesses strong applicability and computational efficiency but also accurately characterizes the progressive transmission process of trust relationships and enables efficient measurement of quantum interference effects.

This paper proposes a quantum group decision model that integrates intuitionistic fuzzy sets to represent the uncertainty of meteorological disaster information, addressing both vagueness and probabilistic uncertainty. This makes it particularly suitable for modeling the complex and dynamic decision-making processes during emergency responses. The model employs regret theory for attribute weight determination and constructs a quantum-like Bayesian network (QLBN), where Deng entropy is applied to measure the mutual interference effects among decision-makers. Decision-makers' weights are determined using grey relational analysis and incorporated as the initial layer in the Bayesian network. The conditional probabilities within the QLBN are derived by integrating attribute weights and regret utility functions, and the alternatives are ranked based on their final quantum probabilities. The effectiveness and stability of the model are demonstrated through its application in emergency alternative selection for meteorological disasters, confirmed by sensitivity and comparison analyses.