Diffusion Models for Cultural Narrative Generation in Virtual Tourism and Audio-Visual Animation Framework under Artificial Intelligence

To address the limitations of virtual tourism, including homogeneous cultural narrative expression, fragmented audiovisual content, and insufficient interactive experience, this study proposed an artificial intelligence-driven audiovisual animation generation framework based on diffusion models and incorporated a cultural semantic control mechanism to improve generation controllability and semantic consistency. Through multimodal semantic alignment, the framework enabled cross-modal collaborative generation of text, images, and audio. Under the guidance of a cultural knowledge graph, a three-level cultural semantic representation comprising cultural symbols, narrative structure, and emotional imagery was constructed, thereby facilitating the generation of virtual tourism content with coherent narrative logic and rich cultural connotations. Experimental results demonstrated that the proposed framework consistently outperformed the comparison models across multiple evaluation dimensions. In terms of generation quality, the model achieved FID (Fréchet Inception Distance) values of 18.67-19.31 across different data subsets, substantially lower than those of the baseline methods. The IS (Inception Score) increased to 8.13-8.42, indicating superior image clarity and content diversity, while the CLIPScore (Contrastive Language-Image Pretraining Score) reached 0.40-0.44, reflecting stronger semantic alignment between textual and visual modalities. Regarding cultural narrative consistency, marked improvements were obtained in CSC (Cultural Semantic Consistency) (0.79-0.85), NCS (Narrative Coherence Score) (0.77-0.83), EAI (Emotional Alignment Index) (0.79-0.86), and CAS (Cultural Authenticity Score) (87.68-90.33). This indicated that the cultural semantic control mechanism effectively enhanced the accuracy of cultural symbol representation, narrative coherence, and emotional consistency. Subjective evaluation further supported the effectiveness of the proposed approach. Higher MOS (Mean Opinion Score) ratings (4.23-4.41), immersion scores (8.48-8.73), and user engagement values (0.86-0.89) were achieved compared with the competing methods. In addition, the audiovisual synchronization rate exceeded 92%, demonstrating superior audiovisual coordination and interactive experience. Ablation experiments further revealed that removing the cultural semantic control module resulted in substantial reductions in CSC and CAS, whereas excluding the audiovisual synchronization mechanism noticeably weakened immersion and temporal consistency, thereby confirming the effectiveness and necessity of both components. These findings indicated that the proposed framework improved the quality of virtual tourism content generation and substantially strengthened cultural expression, narrative coherence, and user experience. The integration of cultural semantic modeling with diffusion-based generative models was therefore demonstrated to be an effective solution, providing a practical technical paradigm for multimodal content generation in cultural communication.