Prediction of Reader Lending Trend in Academic Library by Linear Regression Modeling postprint

Abstract: [Purpose/significance] By means of the classification and circulation data of library collection, the paper finds the close correlation between reader characteristics and library collection circulation, establish the relationship model. And through model fitting and prediction, this study explores the implicit rule between reader and library circulation which provides technical and means support for the intelligent management of library.[Method/process] Firstly, this paper used clustering and correlation analysis techniques to extract the macroscopic observable characteristics of readers, constructed the direct and indirect mapping relationship between reader characteristics and book classification, and then constructed the regression model of the circulation of reader characteristics and classified books, and verified the validity of the model and optimized the goodness of fit of the model. According to the effective model, this paper explored the trend change of library circulation, and sum up the underlying rules of knowledge construction of the macroscopic characteristics of readers, as well as the impact on the circulation of books.[Result/conclusion] There are 3 classification characteristics of readers, namely, the professional learning direction representing the social role requirements of readers, the enrollment batch representing the interaction effect between readers and the number of readers, which can effectively fit and predict the book circulation. The prediction results show that the model has high accuracy and can be used as an effective tool to provide reliable technical support for library to develop knowledge service.