With the rapid development of computer network information technology, data mining technology has begun to be widely used in all walks of life. In the field of Library and information technology, data mining technology can efficiently analyze and screen a large amount of information. This paper mainly studies the useful content, readers' interests and reading habits in the database. Firstly, it theoretically expounds the literature review of text classification methods and association rule-based algorithms by scholars at home and abroad. Then the data mining technology is used to establish the relevant models in the field of Library and information. Finally, the experimental results show that economics, geography, environmental resources, language, industrial technology and literature are the types of books that readers often consult. These five categories are a collection of common books, with a confidence level of more than 50%. Although the support of public project sets decreases with the increase of the number of public project sets, the average support of the five public project sets is 30.2%, which is higher than the minimum support of public project sets. Therefore, there is a strong correlation between the categories of books in the five project collections in the library.

Hui Wang. Research Hotspots of Data Mining Technology in the Field of Library and Information. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 8: 23-28. https://doi.org/10.25236/AJCIS.2021.040805.

[1] Wang C, Qi H. Visualising the knowledge structure and evolution of wearable device research [J]. Journal of Medical Engineering & Technology, 2021, 45(3):1-16.

[2] Geest K V D, Warner K. Loss and damage in the IPCC Fifth Assessment Report (Working Group II): a text-mining analysis[J]. Climate Policy, 2019(596):1-14.

[3] Tapete D, Cigna F. InSAR data for geohazard assessment in UNESCO World Heritage sites: state-of-the-art and perspectives in the Copernicus era[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 63:24-32.

[4] Cheng Y, Huang A, Qi G, et al. Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System[J]. IEEE Access, 2019, PP(99):1-1.

[5] Okeji C C. Research output of librarians in the field of library and information science in Nigeria: a bibliometric analysis from 2000-March, 2018[J]. Collection Building, 2019, 38(3):53-60.

[6] Royal, Institute, of, et al. Education for research in library and information science: a basis for policy analysis in the Nordic countries[J]. Education for Information, 2017, 3(2):83-102.

[7] Wang Y, Zhao Y, Dang W, et al. The Evolution of Publication Hotspots in Electronic Health Records from 1957 to 2016 and Differences Among Six Countries[J]. Big Data, 2020, 8(2):89-106.

[8] Dwivedi S K, Tripathi R. Exhausting Agile Processing and Data Mining in Electronic Commerce[J]. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2019:80-84.

[9] Weng L M, Zheng Y L, Peng M S, et al. A Bibliometric Analysis of Nonspecific Low Back Pain Research[J]. Pain Research & Management, 2020, 2020:1-13.

[10] Pan X, Zhong B, Wang X , et al. TEXT MINING-BASED PATENT ANALYSIS OF BIM APPLICATION IN CONSTRUCTION[J]. Journal of Civil Engineering and Management, 2021, 27(5):303-315.

[11] Long F, Ning N, Zhang Y, et al. Mining latent academic social relationships by network fusion of multi-type data[J]. Social Network Analysis and Mining, 2020, 10(1):1-16.

[12] Ram, Vinay, Pande, et al. DaMold: A data-mining platform for variant annotation and visualization in molecular diagnostics research[J]. Human Mutation, 2017, 38(7):778-787.