Glass classification and identification based on systematic clustering and BP neural network algorithm

As glass is buried in the ground for years, its surface has been eroded by weathering, thus changing its chemical composition and structure. In this paper, we search for the basis for the classification of high potassium glass and lead-barium glass, and on this basis, we further carry out subclass classification, and finally perform sensitivity analysis on the classification results. After establishing the glass classification model, the artifacts with unknown glass types are predicted, and the data are input into BP neural network for training and prediction to determine their artifact glass types, and sensitivity analysis is performed on the prediction results.