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<p>Qing Dynasty embroidered rank badges, as important carriers of traditional Chinese culture, present challenges in digital expression, cultural gene extraction, and modern application. This paper addresses these issues by, for the first time, constructing an ‘embroidery semantic network’ for structured cultural element expression and innovatively introducing ‘dynamic shape grammar’ for precise form control. This study developed the LoRA-Diffusion-SG three-stage fine-tuning architecture, integrating a multi-source dataset comprising image, knowledge, and craftsmanship layers. This significantly enhances the realism and cultural alignment of generated patterns. Experiments demonstrate the method’s superiority in cultural compliance CLIP similarity 0.78–0.82, symbol recognition accuracy Top-1 82%–85%, and craft feasibility prediction F1-score 0.87–0.89. This study provides technical support for the digital preservation and innovation of embroidery patterns through AI.</p>

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A semantic reconstruction and AI-controlled generation method for the cultural genes of Qing Dynasty embroidery patterns: a case study of official rank badges

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Haiqiong Yang,
  • Qiao Sui,
  • Bing Hu,
  • Kun Shi,
  • Ranran Wang,
  • Maoning Li

摘要

Qing Dynasty embroidered rank badges, as important carriers of traditional Chinese culture, present challenges in digital expression, cultural gene extraction, and modern application. This paper addresses these issues by, for the first time, constructing an ‘embroidery semantic network’ for structured cultural element expression and innovatively introducing ‘dynamic shape grammar’ for precise form control. This study developed the LoRA-Diffusion-SG three-stage fine-tuning architecture, integrating a multi-source dataset comprising image, knowledge, and craftsmanship layers. This significantly enhances the realism and cultural alignment of generated patterns. Experiments demonstrate the method’s superiority in cultural compliance CLIP similarity 0.78–0.82, symbol recognition accuracy Top-1 82%–85%, and craft feasibility prediction F1-score 0.87–0.89. This study provides technical support for the digital preservation and innovation of embroidery patterns through AI.