Neural networks application based on language features in the classification of complex English textbooks granularity

Abstract

The surge in modern information has led to a significant increase in text complexity. To meet the needs of various fields for effective information extraction, research on text complexity grading urgently is urgently needed. The study uses the Flesh-Kincaid Grade Level (FKGL) model to extract language features, selects English textbooks as training corpus, and introduces the Graph Convolutional Network of Attention Mechanism (GCN_ATT) model of attention mechanism to construct a text complexity grading model. The research results indicated that in the 10-fold crossover experiment, GCN_ATT’s accuracy, recall, and F1 all reached over 88%. Compared to multi class logistic regression models, GCN_ATT’s various performance indicators were approximately 2% to 3% higher. Meanwhile, GCN_ ATT’s F1 standard deviation decreased by 0.7% and 1.78% compared to the other two models. In addition, GCN_ATT’s fluctuation range of recall and accuracy was less than 20%, a decrease of 12% and 18% compared to the ordered multi classification regression model. Explanation based on GCN_ATT’s text complexity grading has higher accuracy and more stable performance, providing an effective method reference for current text complexity grading problems.