Knowledge-Driven 3D Content Generation: A Rule+LLM-Verify-Based Method for Constructing a Tibetan Cultural and Tourism Knowledge Graph

The digital transformation of Tibetan cultural tourism is hindered by high manual costs, weak semantic adaptability, and cultural security risks. To address these, this paper proposes RLT2C, a “Rule+LLM-Verify” approach to automated and culturally secure KG construction. It employs a lightweight-large model collaboration mechanism, where a fine-tuned lightweight model generates initial Cypher statements, rigorously verified by LLMs for local semantic accuracy and cultural compliance. This two-stage process, combined with a dynamic-static cultural constraint system, ensures high efficiency and preserves cultural integrity, supporting knowledge-driven naked-eye 3D immersive experiences. Experimental results on 1200 Tibetan tourism-related texts show that RLT2C outperforms baselines in construction efficiency (14.5 triples/100 words), relationship accuracy (91.5%), local semantic adaptability (87.9%), and graph redundancy rate (5.4%). RLT2C exhibits strong practicality and scalability. The constructed KG serves not only as an information repository but also as a foundational engine for immersive visualization. By acting as a “central index” for 3D assets and a “safety gatekeeper” for content generation, it enables the dynamic and secure rendering of culturally authentic naked-eye 3D experiences from natural language queries.