Analysis of College Students' Network Moral Behavior by the History of Ideological and Political Education under Deep Learning.

Abstract

The research on the history of ideological and political education (IPE) is the basis for deepening it, and it is also of great help to higher education. The diversity of network information also easily leads to poor guidance for college students who are not strong in discrimination. This study adopts the method of a questionnaire survey to investigate the common moral anomie among college students in the network space. The survey data are sorted and classified and then input into the recurrent neural network structure for data analysis using deep learning (DL) algorithms. The results are fed back to the investigators intuitively and understandably. The results show that some college students have some problems, such as lack of network moral knowledge, vague values, moral behavior anomia, spatial knowledge and behavior inconsistency, and moral and emotional indifference. DL algorithms are added to the analysis process to make the findings more objective. These conclusions provide reference suggestions for subsequent research on college students' online moral behavior in the context of IPE history.