Textbook Classification Method of Index of Moral Education Based on Deep Learning

Abstract

Abstract: Moral education is the cornerstone of personal development and one of the important responsibilities of schools. As an important carrier of moral education, the index of moral education has naturally become one of the important standards for textbook revision. It is more efficient and reliable to use deep learning to realize automatic classification of textbook titles. However, the text data set of the textbook has the characteristics of abundant text information, unobtrusion of features and unbalanced sample distribution. To solve these problems, a new data enhancement method is combined. According to the contribution of text words vector to the classification results, the attention matrix is calculated by the attention mechanism, and then the word vector matrix is input into the model together, a text classification model IoMET_A bonded with attention mechanism is proposed. IoMET_A is used to study the textbooks of primary and secondary schools in Shanghai. The experimental results show that compared with the original IoMET text classifier, IoMET_A effectively improves the evaluation effect.

Key words: index of moral education, Chinese text classification, attention mechanism, convolutional neural network, IoMET_A

GUO Shu-wu, CHEN Jun-hua. Textbook Classification Method of Index of Moral Education Based on Deep Learning[J]. Computer and Modernization, 2021, 0(09): 106-112.

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