Abstract

Existing Kansei-oriented product design studies often rely on simplified styling–color associations or weighted matching of perceptual imagery, making it difficult to capture users’ multi-affective requirements. To address this limitation, this study proposes a product form innovation design method based on a multi-domain coupling network model and establishes a mapping framework from target Kansei images to design solutions. Taking electric mopeds as a case study, the KJ method, k-means clustering, fuzzy Kano analysis, probabilistic hesitant fuzzy entropy weighting, and game-theoretic weighting were employed to identify the target Kansei image. Product form was then decomposed into styling, color, and material domains, and a multi-domain coupling network was constructed based on a form-element co-occurrence matrix. Through topological analysis and the LH-index, 14 key form elements associated with the target image were identified. Quantification Theory Type I was subsequently applied to establish the mapping relationship between form elements and Kansei images, while the Bee Evolutionary Genetic Algorithm (BEGA) was used to generate optimal design solutions. The results indicate that “elegance” is the dominant Kansei image for electric mopeds. The proposed model achieved strong predictive performance (R2 = 0.923), and the optimized design obtained an 85% user recognition rate, showing high consistency across multiple validation methods. The proposed framework effectively reveals the coupling effects of styling, color, and material and provides a systematic approach for data-driven product form innovation.

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