Teaching Reform of Ancient Literature Based on Credible BP Neural Network Technology in New Media Environment

Abstract

Recent years have seen a progressive integration of the new media environment into people's lives, studies, and employment, which has had a profound impact on China's social economy. It is unavoidable to conduct the educational reform of classical literature in the age of new media. Data mining (DM) is a method for analysing and learning from data in databases and data warehouses using artificial intelligence. They work better together to create a solid foundation for decision-making analysis of businesses or pertinent departments in many fields. The BP algorithm can cause the weight to converge to a specific number, but it cannot ensure that that value is the error plane's overall minimum. In order to correct the flaw that the BP neural network is prone to falling into local minima, the improved credible BP neural network used in this paper adopts the momentum factor. The findings demonstrate that there is little variation between the predicted value and the actual value achieved by applying the BP algorithm in the experimental group with order of magnitude standardised operation, and that this algorithm's error has been decreased by 7.38%. It demonstrates that by examining students' test scores in a data warehouse with a neural network algorithm in DM, we may discover potential patterns among the data.