{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:16:44Z","timestamp":1741753004610,"version":"3.38.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>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\u2019s accuracy, recall, and F1 all reached over 88%. Compared to multi class logistic regression models, GCN_ATT\u2019s various performance indicators were approximately 2% to 3% higher. Meanwhile, GCN_ ATT\u2019s F1 standard deviation decreased by 0.7% and 1.78% compared to the other two models. In addition, GCN_ATT\u2019s 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\u2019s text complexity grading has higher accuracy and more stable performance, providing an effective method reference for current text complexity grading problems.<\/jats:p>","DOI":"10.3233\/idt-230448","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T15:15:20Z","timestamp":1718723720000},"page":"855-866","source":"Crossref","is-referenced-by-count":0,"title":["Neural networks application based on language features in the classification of complex English textbooks granularity"],"prefix":"10.1177","volume":"18","author":[{"given":"Hao","family":"Wu","sequence":"first","affiliation":[]}],"member":"179","reference":[{"issue":"3-4","key":"10.3233\/IDT-230448_ref1","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1111\/1467-9817.12283","article-title":"Moving beyond classic readability formulas: New methods and new models","volume":"42","author":"Crossley","year":"2019","journal-title":"Journal of Research in 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