An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

Abstract

Ideological and political education can improve people’s ideological and moral character and psychological quality, improve social governance, and promote the harmonious development of society. However, the current data of ideological and political education have the trend of mass and diversification. In order to improve the effect of ideological and political education, this paper proposes an analysis method of the historical process of cultural self-confidence of ideological and political education based on in-depth learning. By preprocessing the ideological and political education data to obtain text keywords, the principal component analysis method is improved to reduce the dimension of the ideological and political education data, and the vector space model is used to complete the text balance processing of the reduced dimension data. The data sensitivity after dimensionality reduction is analyzed by data training, the historical text data mining model is constructed to extract data features, and the bidirectional recurrent neural network is used to complete data extraction. The semantic features of the extracted data are obtained by using the forward and reverse feature generation methods, and the historical process of the cultural confidence of ideological and political education is analyzed by using the two-way generation method. The experimental results show that the accuracy rate of the historical process analysis method of cultural self-confidence in ideological and political education based on deep learning is 95%, the recall rate is 94%, and the degree of collaborative performance is good.