Title: A classification method of reader borrowing data information in modern library based on top-k query algorithm

Authors: Wei Huang; Jing Ling

Addresses: Library, Hunan City University, Yiyang, 413000, China ' Library, Xiangnan University, Chenzhou, 423000, China

Abstract: In order to overcome the problems of low accuracy of classification results and long classification time in the traditional classification method of modern library reader borrowing data information, a modern library reader borrowing data information classification method based on top-k query algorithm is proposed. First of all, top-k query algorithm is used to collect library readers' borrowing data information and preprocess it. Then, combining the information gain algorithm and the maximum correlation and minimum redundancy algorithm, the second feature selection is performed for the data information borrowed by readers. Finally, the polynomial naive Bayesian model is used to realise the classification of library readers' borrowing data information. The experimental results show that the classification results using this method are accurate, the classification time is always within 11 s, the classification effect is good, and the application performance is good.

Keywords: top-k query algorithm; modern library; readers borrow data; information classification; maximum correlation minimum redundancy algorithm; polynomial naive Bayesian model.

DOI: 10.1504/IJRIS.2025.146933

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.2, pp.138 - 145

Received: 22 Feb 2023
Accepted: 15 May 2023
Published online: 27 Jun 2025 *

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