Application of machine learning algorithm in electronic book database management system

• Published in: SN applied sciences Vol. 5; no. 11; pp. 287 - 11
• Main Authors: Zhou, Jianghe, Huang, Tangsen
• Format: Journal Article Book Review
• Language: English
• Published: Cham Springer International Publishing 01.11.2023; Springer Nature B.V; Springer
• Subjects: Academic libraries; Algorithms; Applied and Technical Physics; Archives & records; Automation; Borrowing; Chemistry/Food Science; Classification; Classifiers; Crashes; Cultural heritage; Data base management systems; Data processing; Database management; Earth Sciences; Empirical analysis; Engineering; Environment; Interdisciplinary subjects; Learning algorithms; Librarians; Libraries; Library management; Library resources; Library special collection resources; Linear regression; Machine learning; Machine learning algorithm; Materials Science; Performance enhancement; Regression analysis; Regression models; Support vector machines; Library special collection resources; Database management; Linear regression; Machine learning algorithm
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-023-05508-3

Intelligent algorithms have excellent performance in managing large databases and mining effective knowledge, and many researchers have introduced these algorithms into research to improve the performance of book management systems. This article introduced the application of machine learning algorithms in electronic book database management systems. A linear classifier (linear regression algorithm is one of machine learning algorithms) was used to analyze the empirical loss value. This paper studied the application of machine learning algorithm in the database management of library special collection resources, aiming to optimize the management of library special collection resources database through machine learning algorithm, and provided students with better experience of using special collection resources. This paper verified the effectiveness of the machine learning algorithm from three aspects: the retrieval time before and after the special resource database, the number of crashes in a month and the number of library staff. The average time of the five sets using the machine learning algorithm was about 150% faster than the average search time of the five sets of special resource databases. The number of crashes after using machine learning algorithm was 2, 1, 1, 2, 1, which was much lower than that of special resource database. After using the machine learning algorithm, the five groups of workers were 7, 5, 9, 6, and 4, respectively, far fewer than the five groups of special collectors. Finally, by comparing the linear regression model, Support vector machine model, and random forest model, it was found that the accuracy of the linear regression model reached 98.2%, an increase of 8.2% compared to the random forest model, the precision rate reached 96.7%, and the recall rate reached 98.2%. 97.8%, F1 value reached 97.5%.It can be seen from the experimental data that the machine learning algorithm plays a good role in the database management of the library special collection resources. Article Highlights Through linear regression algorithm, it can be identified by features, so this paper establishes an attribute linear classifier for each attribute. This paper found that machine learning algorithm can reduce the crash problem of special resource database. This paper found that machine learning algorithms improve the efficiency of a database dedicated to collecting resources, thereby saving human resources.