A User Demand-Driven Product Concept Design Framework Assisted by AIGC: A Case Study of Outdoor Dining Facilities

With the expansion of urban public spaces and outdoor dining activities, users increasingly demand functional, comfortable, and intelligent outdoor dining facilities, yet traditional design methods rely on subjective manual investigation and struggle to capture real-time user needs. Therefore, this study proposes a product design framework combining online review mining with Artificial Intelligence Generated Content (AIGC). User requirements are first extracted from online reviews through text mining and Latent Dirichlet Allocation (LDA) topic modeling. Subsequently, Quality Function Deployment (QFD) maps these requirements to design features to prioritize key features. A mapping mechanism then translates these elements—such as sunshade and rain protection, shared dining experiences, and multifunctional barbecue facilities—into structured AIGC prompts for concept generation. Taking outdoor dining facilities in parks, scenic areas, and commercial outdoor dining spaces as case studies, the results demonstrate that the proposed framework improves the generated design concepts in terms of functional completeness, environmental adaptability, and structural rationality, leading to concept designs that better satisfy user requirements for outdoor dining facilities. The proposed framework provides a user demand-driven approach for systematically transforming user requirements into structured AIGC prompts for the concept design of public facilities.