Research on the classification of ancient silicate glass artifacts based on machine learning

Classifying cultural relics has always been a major challenge for archaeologists. Using glass artifacts as the research object, a classification model for glass artifacts was constructed using decision trees, support vector machines, and logistic regression methods based on their patterns, colors, surface weathering conditions, types, and composition ratios. Three models were used to identify the types of unknown glass artifacts. A subclassification model for high-potassium glass and lead barium glass was established using the K-means clustering method. The elbow method and average contour method were used to determine the optimal number of clusters, and the decision tree model was named based on the characteristics of the cluster center components. The research results indicate that the three models yield consistent identification results for unknown types of glass relics, and the classification results are good. Lead barium glass and high-potassium glass can be divided into three and six subclasses, respectively, and the naming of the subclass decision tree is reasonable. The identification method for ancient glass relics in this article is highly practical and can provide a reference for the classification and identification of other component data.