To address issues including generally low user satisfaction in the outdoor apparel market, the lack of research on lightweight outdoor apparel for urban travel, and narrowly focused studies, this paper proposes a user-centered design approach. The approach integrates the Analytic Hierarchy Process (AHP), Kansei Engineering (KE), and Artificial Intelligence-Generated Content (AIGC) for improved apparel design. First, the Analytic Hierarchy Process (AHP) was used to construct a hierarchical index of user requirements, highlighting key elements such as garment structure and craftsmanship (weight: 0.4103) and material performance (weight: 0.3184). Second, a Kansei word set was developed, and semantic differential scales combined with Principal Component Analysis (PCA) were employed to analyze data collected from 100 user evaluations, identifying specific design features under the dimensions including (1) structure and craftsmanship and (2) materials. Subsequently, high-priority user needs and recognized design features were converted into actionable design instructions via an AIGC platform, facilitating the intelligent development and optimization of design ideas based on representative samples. Finally, the effectiveness of the optimized design proposals was validated through feedback from target users. The results demonstrate that the proposed AHP–KE–AIGC integrated model effectively converts users’ weighted requirements and ambiguous Kansei preferences for urban travel light outdoor apparel into specific visual design elements, significantly improving user satisfaction with the optimized design schemes compared with conventional outdoor apparel design approaches (mean score > 1). In summary, the integrated pathway for user requirement translation and design generation proposed in this study is applicable not only to light outdoor apparel design but also shows potential for broader application across other apparel categories and related product design domains.
Qiu et al. (Sun,) studied this question.