Optimization and experimentation of book classification algorithms based on BERT

Zhizhe Xue; Fenglong Fan

doi:10.1117/12.3039383

4 September 2024 Optimization and experimentation of book classification algorithms based on BERT

Zhizhe Xue, Fenglong Fan

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592O (2024) https://doi.org/10.1117/12.3039383
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhizhe Xue and Fenglong Fan "Optimization and experimentation of book classification algorithms based on BERT", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592O (4 September 2024); https://doi.org/10.1117/12.3039383

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