Intelligent Service Book Sorting in University Libraries Based on Linear Discriminant Analysis Method

The demand for intelligent services in university libraries is constantly increasing, especially in the intelligent book sorting work. The research aims to explore an intelligent classification method for university library books based on linear discriminant analysis. It is used to reduce the dimensionality of feature multidimensional data. A membership model for different categories of books is established to achieve classification. The results showed that when the training set data was reduced to two-dimensional, the feature extraction accuracy of the classification algorithm reached 64.02%, which was significantly higher than 52.48% of one-dimensional data. In addition, the membership calculation accuracy of axiomatic fuzzy sets on two-dimensional data was high, reducing the classification difficulties caused by mixed samples. After comparing and analyzing different algorithms, the proposed transfer learning linear-discriminant analysis-axiomatic fuzzy set algorithm achieved the highest accuracy of 98.67% and completed data classification in about 20s, which was superior to other commonly used classification algorithms. The practical significance of the research lies in providing an efficient and accurate book sorting algorithm, which helps to improve the work efficiency and service quality of libraries.

@article{Wang2024,
  title     = {Intelligent Service Book Sorting in University Libraries Based on Linear Discriminant Analysis Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Changjun Wang and Fengxia You and Yu Wang},
  doi       = {10.14569/IJACSA.2024.0151042},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151042}
}