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• A criteria clustering and weighting approach based on three-way decision making. • Uncertain evaluation information is modeled objectively based on clustering results. • An incomplete information fusion approach based on interval D-S evidence theory. • Consideration of decision risk can improve the objectivity of evaluation results. • Consideration of information conflicts can improve the reliability of results. Concept evaluation plays a pivotal role in complex mechatronic product development, and its results greatly influence the subsequent design and manufacturing. However, the inclusion of large-scale criteria introduces complex calculations and coupling relationships, which will affect the evaluation. Moreover, the limited availability of early design knowledge, risk attitudes and cognitive differences of decision makers (DMs) lead to incompleteness and uncertainty in evaluation information. To address these challenges, a concept evaluation approach based on incomplete information with considering large-scale criteria and risk attitudes is proposed. Initially, an evaluation criteria clustering approach based on interval three-way decision-making is proposed, which considers DMs' cognitions and risk attitudes. Second, the uncertainty model of evaluation information is established according to the criteria cluster result, and the incomplete evaluation information is fused using the interval Dempster-Shafer evidence theory which takes conflicting evidence into consideration. Third, the optimal concept is selected using the combined criteria weighting approach and the interval acronym in Portuguese for interactive and multi-criteria decision making (I-TODIM). Finally, a pipe inspection robot is utilized as an engineering case to verify the effectiveness of the proposed approach. Approach comparison and sensitivity analysis demonstrate the reliability and robustness of the proposed approach when applied to large-scale criteria concept evaluation. [ABSTRACT FROM AUTHOR]