Study and Identification of Ancient Glass Composition Based on Regression Equation and Statistical Analysis

Abstract

Glass is valuable material evidence of the early trade along the ancient Silk Road, but the ancient glass is easily weathered by the buried environment, which leads to the change of its composition proportion, thus affecting the correct judgment of its classification. The study of the composition analysis and identification of ancient glass products is of great help to the understanding of the social culture at that time and the trade civilization between China and foreign countries. This paper mainly studies the composition analysis and identification of ancient glass products. Firstly, the chi-square test is used to analyze the correlation, the regression analysis model is established to complete the significance test, and dynamic clustering is used to classify the glass. It is concluded that high-potassium glass and lead-barium glass can be divided into three categories. The rationality and sensitivity of the classification results are analyzed by a decision tree. Finally, based on the least square method and dynamic clustering, the results show that: The comprehensive model used in this paper can accurately analyze the composition of ancient glass.