In view of the uncertainty in the decision-making process of product industrial design and the difficulty of accurately describing the result of overall decision-making through a single design decision-making stage, a three-parameter interval gray number was introduced to describe the opinions of decision makers, and a Bayesian network (BN) model was constructed to learn the users’ decision-making information about the existing mature products in the market. In doing so, the state distribution probability of the target product design schemes on each index could be obtained. To reflect the psychological behavior of decision-makers’ perception of the relative gains and losses about design schemes, the prospect theory (PT) and BN were integrated to construct the prospect functions of product industrial design schemes in different decision-making stages. In addition, an optimization model was built based on the cognitive progression assumption to calculate the weights of multistage decision-making information in product industrial design. The comprehensive prospect values were computed to help identify the pros and cons of the product industrial design schemes. The effectiveness of the method was verified through the case study of the multistage decision-making information fusing of numerical control grinder industrial design. Results show that the proposed method can help introduce the multistage opinion preference of users to estimate the probability distribution of design decision-making indexes, realize decision-making information fusion with prospect values of product industrial design schemes, and improve the quality of design decision-making in an overall and scientific way.