Application of deep learning for transformation of Chinese traditional cultural narrative patterns and enhancement of cultural identity empowered by AIGC.

This study aims to achieve controllable generation of Chinese traditional cultural narrative content and enhance cultural identity. First, it constructs a tri-modal generation framework of text-image-style based on Stable Diffusion v2.1 and Contrastive Language-Image Pretraining (CLIP) models, realizing the joint modeling of traditional cultural semantics and visual imagery. Second, the study introduces the Low-Rank Adaptation (LoRA) mechanism to embed traditional cultural style features in a lightweight manner, improving the model's style adaptability under small sample conditions. Finally, a three-level evaluation system of "generation quality-semantic consistency-cultural identity" is built, covering both objective indicators and user feedback, to systematically verify the model's performance. Results show that the proposed model significantly outperforms existing methods in multiple dimensions: in terms of image quality, the Fréchet Inception Distance (FID) is 22.85, the Learned Perceptual Image Patch Similarity (LPIPS) is 0.298, and the style recognition accuracy reaches 86.4%. Regarding narrative consistency, the Bilingual Evaluation Understudy (BLEU) score is 0.325, the CLIP text-image similarity is 0.793, and the Narrative Style Match is 82.3%. On the cultural perception level, the average user narrative resonance is 4.32 points, the imagery accuracy score is 0.748, and the question-answer task pass rate is 82.6%. The comparative results indicate that the proposed method has significant advantages in expressive diversity and depth of cultural communication. When properly designed, Artificial Intelligence Generated Content (AIGC) technology can be effectively used for the generation and identity reconstruction of Chinese traditional cultural narrative content. This study provides a scalable technical path for the integration of AI and traditional culture, and expands the boundaries of digital humanities in content generation and reception research.