The Classification and Identification of Ancient Glass Artifacts Based on Decision Tree and Cluster Analysis

The analysis of ancient glass compositions is crucial for accurately identifying artifact categories. This study establishes a classification standard for high-potassium glass and lead-barium glass by constructing a decision tree model, with PbO content less than 5.46% being classified as high-potassium glass, and the opposite as lead-barium glass. Furthermore, cluster analysis is used to further subdivide high-potassium glass into high-silicon and low-silicon categories, and lead-barium glass into high-copper and low-copper categories. This method not only helps to accurately identify unknown types of glass, improving the precision of manual classification, but also provides a reliable scientific basis for historical research.