BookGCN: a book classification model based on text attributes and link relationships

In the digital era, the transformation of the book industry has promoted the rapid development of book e-commerce platforms, emphasizing the importance of accurate classification of book categories. This paper proposes a deep learning model BookGCN that combines text attributes and book link relationships, aiming to improve the accuracy of book classification. By using Bert for text feature extraction and Graph Convolutional Network (GCN) to extract the link relationship between books, the model effectively integrates these two types of information. Experimental results show that the SAGE graph convolution model performs best in different graph convolution networks, reaching an accuracy of 0.602. At the same time, the ablation experiment verified that the link relationship and residual connection significantly improved the performance of the model. Although there is a long-tail problem in the distribution of categories, this paper not only provides new ideas for book classification, but also lays a foundation for the development of personalized recommendation system, and injects new vitality into the sustainable development of the book industry.