Experience-based book recommendation systems suffer from blurred boundaries and unguaranteed quality. In China, there is no reasonable and credible book recommendation system for elementary school students based on big data and expert systems. The basis of book recommendation is text classification. In this paper, we first construct a hierarchy dictionary to encode books according to the textbook read by pupils in different grades. We then classify extra-curricular books into lower grade, upper grade, and non-elementary classes. Four machine learning methods and a deep learning algorithm are used to classify the text. The model metrics are evaluated by Accuracy, Recall, F1-score. Among them, both deep learning and machine learning have a good performance on binary classification tasks, with the accuracy of the LR method reaching 98.5%. This reflects the value of our construction of a hierarchical vocabulary. In addition, accuracy for the triple classification task generally achieves 75%.
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