Product designers need to fully understand consumers’ emotional preferences and responses for product forms to improve products. However, users and designers have different understandings and concepts in the product evaluation process, which will lead to cognitive asymmetry in the product design and evaluating process. This phenomenon prevents designers to grasp users’ needs, increasing the risk of product development failure. To this end, this paper proposes a product evaluation method that combines natural language processing techniques and fuzzy multi-criteria decision-making into a new integrated way to reduce the cognitive difference between users and designers, so as to solve the problem of cognitive asymmetry. This was done firstly by obtaining the review data of products from users on the Internet, based on a web crawler, and then constructing word vectors based on natural language processing techniques to realize the parametric expression of the Kansei image. Secondly, by using a statistical method to extract the product scheme that meets the preferences of users and designers, and then quantifying the relationship between the product form and Kansei image based on a grey relational analysis (GRA). Finally, by calculating the indicator weight based on the Entropy method and using the fuzzy TOPSIS method to explore the prioritization of the product design alternatives in view of the Kansei needs of users. Taking the smart capsule coffee machine as an example, the feasibility and effectiveness of this method are verified. In particular, the method proposed in this research can not only enable different cognitive subjects to achieve cognitive symmetry, but also filter out product forms that meet the cognitive needs of users. Moreover, this study provides a theoretical basis and practical significance for reducing the cognitive differences between cognitive subjects in the whole process of product design, and provides a systematic framework for the industry to effectively connect customer needs and product design decisions. At the same time, this study has introduced a new method for Kansei engineering.