Application of dunhuang caisson patterns in cultural and creative product design based on artificial intelligence generated content technology

The rapid advancement of artificial intelligence generated content (AIGC) provides new technological pathways for digitally preserving and reinventing traditional patterns. This study introduces an integrated framework to explore the creative reproduction of Dunhuang caisson patterns and their application in product design. By coupling the analytic hierarchy process (AHP) with low-rank adaptation (LoRA) fine-tuning, the proposed methodology translates cultural hierarchies into quantitative generative constraints. Firstly, the AHP was employed to establish an evaluation system for the Dunhuang caisson patterns, ranking the key patterns such as lotus and flowers according to their relative importance. Based on these results, representative patterns were collected and preprocessed from published books, academic literature, and open-source datasets to construct a dataset. Subsequently, the weights obtained from the AHP were converted into attention-guided and gradient-guided mechanisms, and the LoRA technique was used to fine-tune three mainstream models (Stable Diffusion 1.5 (SD 1.5), Stable Diffusion XL (SDXL), and Flux model). The performance of the fine-tuned models in terms of pattern generation quality and cultural symbol representation was evaluated. Finally, the generated patterns were applied to four representative cultural and creative products (mugs, carpets, handbags, and cushions). The design of the mugs was further examined through Importance-performance analysis (IPA) to verify the practical feasibility and performance of the generated output. This study not only enhances the automated generation quality of Dunhuang caisson patterns but also provides a transferable technical framework for the digital preservation and innovative utilization of traditional patterns in product design. • Integrates AHP to quantify the cultural semantics of Dunhuang caisson patterns. • Uses AHP-derived ratio to guide LoRA fine-tuning of SD1.5, SDXL, and Flux models. • LoRA-fine-tuned models outperform base models in detail, structure, and symbolism. • Applies generated patterns to four cultural and creative products and validates via IPA. • Establishes a reproducible three-stage AIGC workflow for cultural design innovation.