In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a method of cognitive matching of the design subjects. First, we use the relevant methods of Kansei engineering to quantify the Kansei image cognition of the two design subjects and construct a cognitive matching model of the design subjects with information entropy and the technique for order preference by similarity to ideal solution (TOPSIS). Second, according to the Kansei image, the Kansei image prototype cluster is constructed, and the representative Kansei image prototype is obtained. Then, we combine an artificial neural network (ANN) with a cognitive matching model of the design subjects to construct a product Kansei image evaluation system; this is used to evaluate the evolved forms. Finally, a product Kansei image form evolution system is constructed based on the genetic algorithm (GA). To some extent, the system simulates the cognitive matching process between designers and users in product design, helps designers to more accurately understand the cognitive trends of the two design subjects, and provides a theoretical basis for the intelligent design of product forms through the cognitive balance of multiple design subjects. This paper takes a beverage bottle as an example to verify the feasibility of the model through a comparative study.

Product serialization design is an effective method for product family development. To explore the development law of product serialization, the kansei image change law within the series, and the interaction mechanisms of kansei image and form between the series, this paper proposes an evolutionary design method of product form inspired by Spider-webs. Inspired by the special structure and mechanical properties of Spider-webs in nature, at the macro level, we use the structure of the Spider-webs to describe the relationship between products in the product family. At the micro level, based on the mechanical properties of spiderwebs, we analyzed the development law of the product form within and between the series in the product family and then proposed a calculation method for the crossover coefficient and variation coefficient (in the genetic algorithm) for product form evolution. This method provides a scientific basis for determining the crossover coefficient and the variation coefficient from a new perspective. The case study results show that this model can effectively simulate the changing laws of design cognition and the evolution laws of product form, thereby providing a theoretical basis for the intelligent design of product form in a product family.