In the trade of the ancient Chinese Silk Road, glass was a valuable material evidence of the early trade. However, the glass has experienced historical sand for a long time, and its surface is easily affected by the buried environment and weathering. In this paper, we will study the change of elements during weathering and identify and classify the glass based on the corresponding characteristics. Firstly, the component data of the ancient glass relics are processed uniformly, and the correlation analysis is conducted, and the model of the Logistics equation and the SVM subclassification model are trained and optimized with large lot of data to adjust to the best classification model, and the classification results. Through case analysis and comparative study of model results, it is shown that the classification model using potassium, silicon, lead and barium content as indicators has good applicability, accuracy and simplicity, which provides new ideas for the identification and analysis of the chemical composition of glass cultural relics.
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