Abstract: 【Purpose/significance】The prediction of book borrowing data has an important guiding significance for library resource construction and accurate service. This paper collects the ten years' borrowing data of the library management system of a double firstclass university in Northeast China, and forecasts the borrowing trend in the future according to the categories of books and the col⁃leges to which the borrower belongs.【Method/process】This paper uses a hybrid prediction model based on time series to predict the number of books borrowed, in which the hybrid prediction is the combination of monistic time series prediction and multivariate time series prediction.【Results/conclusion】The experimental results show that the time series algorithm is used to predict the borrowing
data of university libraries. From 2008 to 2017, the number of books borrowed has increased from about 300 to nearly 4000. From 2018 to 2021, the number of books borrowed by category D and S has decreased the most, the number of books borrowed by category U has decreased the least, the number of books borrowed by category T and E has increased the most, and the error rate of category Q and X is large, The research conclusion can be used as a reference for the management of university libraries.【Innovation/limitation】There are many researches on the construction of library collection resources and service innovation in the academic circle, but there are relatively few researches from the perspective of monistic time series and multivariate time series prediction. This paper makes up
for the deficiencies in this regard.