With the development of digitalization, the spread of traditional culture in China faces new opportunities and challenges. The rise of AIGC technology provides a new way to solve these problems. This study explores the AIGC content generation and intelligent interaction technology of China traditional culture for personalized recommendation, and constructs a complete technical system of "cultural understanding-content generation-accurate recommendation-intelligent interaction" by integrating AIGC model, personalized recommendation algorithm and multimodal interaction design. Research methods include data acquisition and preprocessing, AIGC model training, personalized recommendation algorithm design and intelligent interactive system development. The experimental results show that the proposed CulturalAIGC model is significantly superior to the general model in the fluency, information integrity and cultural accuracy of text generation and image generation. The hybrid recommendation strategy performs best in recommendation accuracy and ranking quality, and obtains higher user satisfaction; Multimodal dialogue system is significantly superior to traditional rule-based question answering system in task completion rate, average dialogue rounds and user retention rate.
Liu et al. (Wed,) studied this question.