Based on BP neural network glass cultural relics chemical category and composition prediction model construction

Abstract

The classification and prediction of chemical composition of glass cultural relics plays an important role in the study of cultural relics, and this paper obtains the chemical composition survey data of 58 groups of glass cultural relics, obtains the statistical law of the chemical composition content of glass cultural relics, and then builds a neural network prediction model of the chemical composition of glass cultural relics, and uses the chemical composition content of glass cultural relics as the training set and test set for building the prediction model. In this paper, the data is preprocessed and fed into the training set, so that the BP neural network model continues to learn, adjust the training parameters, and finally obtain the optimal prediction model. Comparing the predicted value with the real value shows that the BP neural network model has good accuracy. This experimental result verifies that the BP neural network combination model can effectively predict the category of glass cultural relics and predict the chemical content before and after differentiation, which provides an effective way for relevant departments in China to study the value of cultural relics.