[Objective] This study constructs a hierarchical multi-label classification model for children's literature, aiming to realize the automatic classification of children's books, guiding young readers to select books suitable for their development needs. [Methods] We materialized the concept of graded reading into a hierarchical classification label system for children's literature. Then, we built ERNIE-HAM model using deep learning techniques and applied it to the hierarchical multi-label text classification system. [Results] Compared with the four pre-training models, the ERNIE-HAM model performed well in the second and third hierarchical classification levels for children's books. Compared to the single-level algorithm, the hierarchical algorithm improved the values for the second and third levels by about 11%. Compared to the two hierarchical multi-label classification models, HFT-CNN and HMCN, the ERNIE-HAM model improved the third level by 12.79% and 6.48% in the classification results, respectively. [Limitations] The overall classification performance of the proposed model can be further improved, and future work should focus on expanding the dataset and refining the algorithm design. [Conclusions] The ERNIE-HAM model is effective in the hierarchical multi-label classification for children's literature.
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