Traditional multi-attribute group decision-making(MAGDM) methods are concerned with gathering group experts’ judgment preferences rather than providing insight from the affective cognitive perspective, which has difficulty dealing with group decision making problems with new input alternatives or large data samples automatically. In response, we propose a hierarchical group affective computing model that combines human personalities, mood, and emotional states, which can quantitatively describe affective transitions impacted by persistent external stimuli and evaluate the achievement degree of the MAGDM results. Then, we give a definition of affective cognitive parameters in MAGDM and introduce an affective cognitive MAGDM model to study the influence rule between the group experts’ affective cognitive parameters and decision results. Subsequently, affective cognitive parameters identification algorithms are given based on closed-loop feedback controller tuning principle, helping effectively assist the group decision-making process automatically. Numerical and clinical rehabilitation medical cases are employed to verify the proposed methods, demonstrating the validity of the contributions.
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