<p>In large-scale group decision-making (LGDM), experts often exhibit behavioral heterogeneity arising from diverse cognitive patterns, risk attitudes, and willingness to cooperate, which poses significant challenges to achieving group consensus. To address this issue, this study develops a behavioral heterogeneity-oriented (BHO) consensus model in a non-cooperative environment, aiming to balance expert diversity with the overall level of collective consensus. First, personalized adjustment costs are constructed by measuring experts’ degrees of hesitation and trust-related risk attitudes, and a minimum cost consensus (MCC) model is employed to generate individualized opinion recommendations, thereby facilitating consensus while respecting experts’ behavioral heterogeneity. Second, non-cooperative behavior (NCB) is identified based on the deviation between experts’ actual adjusted opinions and the recommended adjustments. In this process, considering that experts exhibit specific behavioral patterns during preference adjustment, an adjustment deviation measure based on the technique for order preference by similarity to an ideal solution using a modified Mahalanobis distance (M-TOPSIS) is proposed to capture the correlations and distributional characteristics of adjustment behaviors. Meanwhile, a cumulative effect mechanism is introduced to further amplify repeated excessive deviations and adaptively update expert weights, thereby mitigating persistent non-cooperative adjustments. Finally, an application example and simulation analyses demonstrate that incorporating behavioral heterogeneity into the consensus-reaching process (CRP) can effectively reduce consensus costs and enhance consensus levels, confirming the effectiveness of the proposed approach.</p>