Abstract
Based on the artificial neural network, K-Means clustering and decision tree algorithm, this paper constructs a variety of models to study the relevant factors of the surface weathering of glass relics and the changes of various chemical components.In this paper, the statistical results are obtained by processing the data with glass type, texture and color respectively, and chi square test is carried out with weathering type respectively. At the same time, a logical regression classification model is established to analyze the relationship. Secondly, the data are divided into four groups based on the glass type and weathering degree, and the statistical rules are obtained by analyzing the average, median, maximum, minimum and change degree of each group of data. Finally, a multi-stage weathering backtracking model is built based on multihead self-attention to predict the chemical composition content of the detection point before weathering. The accuracy of the test is 99%, and the effect is good.
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