Abstract: Product design, as a critical process that transforms innovative concepts into functional, feasible, and market-adaptive solutions, occupies a central position in the product lifecycle. However, the traditional design paradigm—primarily reliant on human expertise—has become inadequate in addressing the challenges posed by knowledge explosion and rapidly evolving demands. This context calls for a transition toward a more efficient, intelligent, and knowledge-driven full-process design system. Multimodal knowledge graphs and large language models (LLMs), as core technologies of next-generation artificial intelligence, offer notable advantages in unstructured knowledge modeling and cross-modal semantic understanding. In response to these capabilities, a hybrid-driven intelligent conceptual design framework integrating multimodal knowledge graphs and LLMs is proposed to support a full-process closed-loop workflow encompassing semantic modeling, knowledge integration, and design solution generation. Specifically, the framework begins by employing an LLM-based three-tier collaborative modeling mechanism to conduct deep semantic parsing and modeling of design-related textual data. It further incorporates visual-language models and multi-view strategies to semantically model additional modalities such as formulas and diagrams, thereby constructing a unified multimodal semantic space. In the knowledge integration phase, an LLM-based cross-modal entity alignment method is utilized to construct a comprehensive multimodal design knowledge graph. Finally, at the design solution generation stage, conceptual design solutions are generated by integrating knowledge graph-enhanced large language models, and the generated design semantics are utilized to guide rapid prototyping of graphical models, forming a full-process design closed-loop. Taking the structural design of a car body as a representative application scenario, an intelligent conceptual design solution generation and application system driven by multimodal knowledge is developed. Experimental results demonstrate that the proposed framework effectively improves design efficiency, providing support for advancing product design toward greater intelligence.

Key words: multimodal, knowledge graph, large language model, intelligent design, full-process