Leveraging knowledge graphs for renaissance costume matching and cultural transmission

Clothing serves not only as a material entity but also as a crucial medium for cultural transmission and expression. The fashion design of the Renaissance period reflects the social trends and aesthetic pursuits of the time. However, traditional analytical methods are limited in efficiently extracting the cultural connotations embedded in these designs. This study develops a knowledge graph-based costume matching system that integrates cultural background, clothing knowledge, and expert commentary. The hierarchical recommendation model not only provides users with efficient and precise costume matching suggestions but also simplifies the selection process. The system constructs a knowledge graph through text mining and data annotation techniques, and optimizes matching recommendations using a weighting algorithm, ensuring cultural consistency and historical accuracy. Experimental results demonstrate that the system excels in recommendation accuracy, consistency, completeness, and immediacy. With its broad application potential and adaptability, the system is expected to contribute to cultural preservation, design fields.