Automation of Book Categorisation Based on Network Centric Quality Management System

In order to improve the efficiency of automatic book classification, the study uses a crawler to crawl book data from regular websites and perform data cleaning and fusion to build a structured knowledge graph. Meanwhile, the processed data is applied to a pre-trained model to improve it, and migration learning is used to improve the results. Fusion of Multiple Attention Mechanisms, Recurrent Neural Network, and Convolutional Neural Network modules into the classification model and feature fusion is used to enhance feature extraction. In addition, the study designed a pre-trained model architecture to help automatically categorise and manage book resources. The results of this study show a significant improvement in the classification of Chinese books on the Chinese Book L2 Subject Classification, iFlytek, and THUCNews datasets with significant performance improvement. The fusion of long and short-term memory and convolutional network Transformer-based bi-directional encoding models improved the accuracy by 0.19%, 1.54% and 0.42% on the two datasets, respectively, while the weighted average F1 scores improved accordingly. Through wireless technology, the automatic classification efficiency of books is realized and the management ability of the library is improved.

@article{Liu2024,
  title     = {Automation of Book Categorisation Based on Network Centric Quality Management System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Tingting Liu and Qiyuan Liu and Linya Fu},
  doi       = {10.14569/IJACSA.2024.0150827},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150827}
}