Designing the color of electric motorcycle products emotionally based on the dynamic field theory and deep learning

Current research in Product Color Emotional Design (PCED) often encounters limitations due to the ambiguity in users' emotional information expression, potentially compromising the effectiveness of the design scheme. This paper presents a novel PCED methodology that integrates dynamic field theory with deep learning, thus enabling one-stop generation from user feature labels to product color. Consequently, a product color design system is established, capable of generating color design schemes that align with users' emotional needs by solely incorporating user feature labels. This approach mitigates potential inaccuracies in generation results that could stem from users' direct expression of emotional information. The validity and applicability of the proposed method are demonstrated through a case study focusing on the color emotion design of an electric motorcycle.

Automated summary

This paper proposes a novel method for Product Color Emotional Design (PCED) by integrating dynamic field theory with deep learning. The method establishes a system capable of generating color design schemes that align with users' emotional needs based solely on user feature labels.