Diffusion Model-Driven Digital Restoration of Cultural Heritage with High Precision 3D Reconstruction and Quantitative Analysis of Visitor Emotional Experience

The digital preservation of cultural heritage is increasingly critical in the evolving field of computational science, aligning closely with the thematic focus in Computer Science on advanced computational frameworks for societal impact. Traditional methods for digital restoration and 3D reconstruction have primarily relied on isolated visual processing or heuristic modeling, often leading to limited semantic understanding, insufficient temporal context integration, and inadequate emotional engagement analysis for visitors. Such approaches struggle to achieve the holistic, multimodal fidelity necessary for culturally rich artifacts, thus falling short of the interdisciplinary innovation emphasized within the journal’s scope. To address these gaps, we propose a novel framework centered on a Temporal-Contextual Relational Encoder (TCRE) and Semantic-Aware Knowledge Fusion (SAKF) strategy. TCRE jointly models multimodal representations, relational structures, and temporal dynamics of heritage artifacts, while SAKF injects domain-specific knowledge bases through semantic-guided attention, enhancing both interpretability and fidelity. Our diffusion model further supports high-precision digital restoration, coupled with D reconstruction, to maintain authentic artifact characteristics. Moreover, a quantitative module for analyzing visitor emotional experiences based on dynamic embedding spaces is introduced, providing a comprehensive evaluation of user interaction. Experimental results demonstrate significant improvements in semantic coherence, reconstruction accuracy, and emotional impact assessment compared to traditional baselines, confirming the strong alignment with the special issue’s focus on AI-driven cultural heritage technologies.