Research on Classification and Identification of Glass Relics Based on Pearson Clustering and Machine Learning Models

Abstract

Ancient glass is very susceptible to weathering due to the influence of buried environments. In order to classify and identify glass artifacts, this article uses the Pearson cluster analysis model to classify them into four types of high potassium and three types of lead and barium. The chemical cost composition of the classification types is determined, and then the composition of each chemical component is accumulated and arranged. The proportion of each type's content is used as the basis for subclassing. Finally, multiple machine learning methods were used to identify unknown types of glass artifacts, and the results showed that the accuracy of the support vector machine model reached 100%, with a relative improvement of 2% -11%. Therefore, the support vector machine model was considered for classification.