The Construction and Application of Library Intelligent Acquisition Decision Model Based on Decision Tree Algorithm

In today's digital age, libraries, as the core institutions of knowledge management and information services, are facing an increasing demand from readers. In order to provide more efficient, accurate, and personalized interview services, intelligent interview decision-making in libraries has become an important research field. Traditional manual interview services face challenges such as personnel training and knowledge updates, making it difficult to quickly adapt to new needs and changes. To address these issues, research is being conducted on using machine learning technology to perform post pruning on the basis of standard decision trees and combining it with fuzzy logic to design a fuzzy decision tree. The experimental results show that the F rejection rate (FN) of the model rapidly decreases to about 0.1 as the number of training iterations gradually increases, and stabilizes at around 0.05 after 210 rounds of training, which is 0.10 lower than the rule-based decision model FN. The intelligent acquisition decision-making model designed in this study has higher accuracy and stability, and has certain application potential in the field of intelligent acquisition decision-making in libraries.

@article{Pan2024,
  title     = {The Construction and Application of Library Intelligent Acquisition Decision Model Based on Decision Tree Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Hong Pan},
  doi       = {10.14569/IJACSA.2024.0150116},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150116}
}