With the emergence of AIGC, scholars have begun to explore the feasibility of AI-Assisted Design (AIAD) [10]. Kansei Engineering (KE) is a technique that quantifies users' emotional needs and calculates them using mathematical methods, which effectively captures the users' emotional needs and enhances user satisfaction [11]. Since users' emotional needs are subjective and difficult to express, scholars adopt quantitative methods such as questionnaire interviews, user interviews, and survey research to objectively explore user emotional information [12]. As AI technology becomes more prevalent, web crawler tools efficiently gather extensive online data, overcoming the subjectivity associated with small sample sizes [13]. Furthermore, quantifying and analyzing the obtained users' emotional needs will guide the next steps in NPD. Quality Function Deployment (QFD) is a design research method that can transform Customer Requirements (CRs) into product Engineering Characteristics (ECs) [14]. Its core involves inputting CRs on the left wall and entering product ECs on the ceiling of the House of Quality (HoQ) to construct a correlation matrix. Inviting experts to score the correlations will ultimately calculate the priority ranking of ECs that meet CRs [15]. In the traditional QFD method, correlation is represented by a triangle (\(\blacktriangle\)) for 1 point, a circle (\(\Circle\)) for 2 points, and so on [16]. In SMEs, there are relatively few experts, and subjective judgments can introduce uncertainties. Grey set theory is often used to address issues where the sample size is small and the information is unclear [17]. In summary, SMEs lack efficient product design methods that have both established PI and meet user emotional needs during NPD. They also lack the involvement of a large number of experts in R&D and a systematic approach to scientifically address fuzzy and implicit information. More accurately, this study aims to address the following issues:  
Q1: How can AIGC technology be effectively utilized to help SMEs quickly obtain a large number of PI concept sketches? Q2: How can SMEs be helped to quickly obtain and accurately filter customer emotional requirements? Q3: How can SMEs be helped to integrate CR information when screening PI concept sketches? To address these issues, this study proposes an AI-powered method that integrates SG and KE to guide SMEs in new product development. Firstly, SG and Midjourney are used to rapidly generate a multitude of PI conceptual sketches, greatly enhancing the efficiency of the creative stage. Secondly, KE and web crawling techniques are used to obtain a comprehensive understanding of user emotional preferences, assisting enterprises in accurately acquiring CRs. Thirdly, the QFD framework is used to construct the mapping relationship between CRs and PIECs. Experts are invited to score the correlations using interval grey numbers to screen out the PIECs that can most effectively meet CRs. Finally, the design scheme is completed by the selected PIECs. The proposed method helps SMEs quickly obtain product form design schemes that meet PI and user performance with less resource investment, enhancing the competitiveness of the enterprise's brand and products.