This study proposes an intelligent framework for the automated classification and valuation of ceramic artifacts, integrating deep learning and machine learning techniques. An improved YOLOv11 model was constructed to identify key ceramic attributes such as decorative patterns, shapes, and craftsmanship styles. The model achieved a mean Average Precision (mAP@50) of 70.0% and a recall of 91.0%, demonstrating strong capability in detecting complex visual features. Based on the extracted visual attributes, a Random Forest classifier was employed to predict price categories using multi-source auction data, achieving a test accuracy of 99.52%. Feature importance analysis further revealed manufacturing techniques and shape as key predictors of market value. The integrated framework effectively combines visual feature extraction and market-informed valuation, providing a scalable solution for intelligent ceramic appraisal and digital heritage curation. This approach supports both expert and non-expert applications, laying a foundation for future development of intelligent cultural heritage management systems.

This study addresses the challenge of automating ceramic artifact classification and valuation, which has traditionally relied on expert assessments, often introducing subjectivity and inconsistency. By leveraging advanced machine learning and deep learning techniques, the research utilizes the YOLOv11 model to classify key ceramic attributes such as decorative patterns, shapes, and craftsmanship style. A Random Forest classifier is then used to predict the market value of ceramics based on features extracted by YOLOv11, alongside auction data. The model achieved significant improvements, with an mAP@50 of 57%, recall of 91%, and an overall accuracy of 94.36% for price classification. Feature importance analysis revealed that manufacturing techniques played a crucial role in determining ceramic prices. This study demonstrates the effectiveness of combining deep learning for feature extraction and machine learning for price prediction, providing a reliable and automated tool for ceramic classification and valuation, with significant implications for both academic research and practical applications in artifact appraisal and museum digitization.