<p>In the field of affective product design, accurately aggregating preferences from diverse groups is critical for decision-making. However, traditional multi-subgroup decision models often treat user preferences as static inputs, overlooking the structural cognitive bias introduced by sequential sample presentation. This theoretical limitation frequently leads to data distortion and fails to capture genuine consensus within complex design spaces. To address this, this paper proposes a novel decision-making framework based on probability linguistic term sets oriented toward simultaneous lineups. Methodologically, it proposes a Repeated Random Simultaneous Lineups experimental protocol to replace single-sequence evaluation, transforming subjective rankings into stable probability distributions and thereby eliminating order-induced bias. Theoretically, a Choquet-Integrated Borda operator is constructed to quantify nonlinear synergistic effects among evaluation criteria and resolve conflicts across different subgroups. Comparative experiments with three baseline methods confirm the superiority of this paradigm. Traditional sequential and non-repeated methods, affected by random noise, yielded consensus indices of merely 65.5 to 74.66%, failing to extract meaningful group preferences, whereas the proposed method achieved a robust consensus of 77.78%. The experimental results empirically verify that the framework can effectively separate genuine preference signals from cognitive noise. This framework provides a systematic framework for multi-subgroup decision-making and enhances scientific accuracy in complex scenarios.</p>