Optimization and experimentation of book classification algorithms based on BERT
Zhizhe Xue, Fenglong Fan · 2024
The Chinese Library Classification (CLC) number is a crucial identifier for indexing books in libraries, and accurate, efficient, and convenient book classification can greatly enhance the management efficiency of libraries. Addressing the issues of low efficiency and insufficient accuracy in current manual book classification and traditional machine learning methods, this paper proposes a book classification algorithm based on the BERT-TextCNN model to achieve automatic book classification. By integrating TextCNN into the BERT model, it can better capture local features and textual structure information. Experimental results show that the model achieves an average precision of 88%. Compared to benchmark models and traditional manual classification methods, the classification efficiency and accuracy have been significantly improved.