Category identification and composition analysis of ancient glass products based on GA-BP neural network and factor analysis

 Ancient glass is a witness of early trade exchanges, and its study has profound historical significance. In this paper, we analyze the nature and chemical composition data of a batch of ancient glass products to investigate the compositional differences and identification characteristics of different types of glass products. In this paper, we use relevant information to achieve the effect of identifying cultural relics based on chemical composition data by mathematical modeling for a batch of ancient glass products in China (divided into high potassium glass and lead-barium glass), and analyze the correlation between the chemical composition of different categories of glass to compare the differences between classes.