Artificial Intelligence Generated Content (AIGC) technology shows great potential in product design. However, relatively limited research has been conducted on combining theory to guide AIGC technology to enhance the depth and breadth of idea generation. Utilizing the Theory of Inventive Problem Solving (TRIZ) and AIGC technology, a design method of product modeling was proposed, and an aging service robot design was used as a case. Assisted by ChatGPT and Python-based big data crawling techniques, the study explored user needs and refined design goals through high-frequency word analysis, competitor analysis, functional modeling, and causal chain analysis. Automated image acquisition, diffusion model inference, text prompt word organization, and image generation are employed to rapidly derive more targeted design solutions. Technical contradiction analysis and physical contradiction analysis are used to iteratively refine the design solutions iteratively, leading to the development of design concepts and validating the feasibility and rationality of the proposed method. The results indicate that the TRIZ can effectively complement the AIGC by enhancing the practicality of solutions through its systematic problem-solving capabilities, thereby improving overall design efficiency and innovation. This method can potentially accelerate the development of innovative solutions and conceptual designs.