Abstract: 【Purpose/significance】The changes in library borrowing data reflect the key concerns of borrowers at that time, and to a cer⁃ tain extent, can reflect the research hotspots of the entire society. This article aims to establish a relationship model between various fields of book borrowing reservation data in university libraries and social hotspots through a large language model, explore the relationship be⁃ tween borrowing data and social hotspots, and assist in the analysis of social hotspots over a period of time.【Method/process】Firstly, a word segmentation model for book titles is constructed using an encoding decoding structure. A large word segmentation dataset is used for training to obtain the original word frequency. Then, domain matching is performed based on the reader′s department and call number in the field. Finally, the weight of the original word frequency is updated from three perspectives: borrowing frequency, reservation dura⁃ tion, and domain, to obtain the final hot word cloud related to social hotspots.【Result/conclusion】This paper first conducted experiments on the segmentation model, and the experiments showed that the algorithm in this paper had a significantly better F-value than other algo⁃ rithms on the MSR, PKU, and CTB6 datasets. Among them, on the CTB6 segmentation dataset, the F-value of the algorithm in this paper reached 97.18, which is 3.15 percentage points higher than the CRF algorithm. The segmentation algorithm with domain optimization per⁃ formed better on texts with strong professionalism. Then this paper makes an experimental analysis of library borrowing data and reserva⁃ tion data, and shows the progressiveness of the hot word cloud generation framework based on domain segmentation optimization. The ex⁃ periment shows that the hot word generated by the algorithm in this paper can establish a certain relationship with social hot spots【. Inno⁃ vation/limitation】This article studies the field characteristics of book borrowing data and reservation data, and innovatively proposes a borrowing hotspot generation framework based on BERT domain segmentation optimization. Although this paper uses the data field char⁃ acteristics of the library to build a hotspot word cloud generation framework and optimizes the word cloud generation results, there is no quantitative indicator for the performance of hot word cloud generation, and more exploration and research are needed.