In the context of uncertain and complex social network decision-making, the differences in risk preferences among decision-makers (DMs) and the mutual interferences between decision sources have a significant impact on the achievement of group consensus. This paper proposes a quantum group consensus model for risk preference, combining evidence fusion approaches to address this issue. Firstly, a risk matching function is constructed to quantify the risk similarity among decision-makers, and combined with the K-shell algorithm and Shapley value to measure the comprehensive influence of nodes, on this basis, the weights of DMs are calculated with the risk matching degree. Secondly, a group consensus model incorporating quantum interference based on minimum adjustment costs is constructed, considering the interferences between individual and group preferences to dynamically facilitate consensus. In addition, based on the Dempster-Shafer evidence theory, the attribute preferences of the group are integrated, the attributes’ weights are determined, and the advantage values of alternatives are calculated for ranking. Finally, the proposed model is validated through a case study involving the configuration of low-altitude rescue equipment. The robustness and applicability of the model are evaluated via comparative analysis, sensitivity analysis, and simulation experiments. The results demonstrate that the proposed method effectively addresses the interference problem among decision sources under uncertainty, and improves the efficiency and quality of group consensus.