Abstract
The study of ancient glass, as an important branch of scientific and technological archaeology, is an important physical material for exploring the economic, technological and cultural exchanges between China and foreign countries on the Silk Road. It is of great significance for the study of the development of ancient societies and cultural exchanges between China and foreign countries. This paper focuses on the composition analysis and identification of ancient glass products, based on data mining and software analysis, giving the weathering pattern of glass surface, predicting the chemical composition content before weathering, as well as cluster analysis and correlation analysis of different categories of glass and their chemical composition. We also concluded that the composition of silica, potassium oxide and barium oxide in high potassium glass artifacts changed to a great extent before and after weathering, while the composition of silica and lead oxide in lead-barium glass artifacts changed to a great extent, and finally, we constructed a multiple linear regression model for prediction by the data of each chemical composition at the weathering point, and conducted F-test on the model to prove that the prediction models of silica, potassium oxide, barium oxide and lead oxide had a good performance. The F-test of the model proved the reasonableness of the prediction models for silica, potassium oxide, barium oxide, and lead oxide. It can be concluded that the glass type has an influence on the chemical composition of glass, so the glass can be divided into two categories of glass: high potassium and lead-barium, and then the chemical composition of these two categories of glass were analyzed separately by gray correlation analysis to obtain the top five correlation ranking of the chemical composition of each category of glass, and then the differences in the chemical composition of these two categories of glass were compared.
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