The Application of Big Data Analysis Methods in Classical Catalog Studies

In the 21st century, the arrival of the era of big data has brought earthshaking changes to the development of bibliography, and the needs of users for directory information have also undergone significant changes. How to reasonably and efficiently disclose massive network information resources has become a new mission of bibliography, and bibliography is facing a transformation opportunity. At present, how to develop and meet the information needs of users is the most important task in the field of bibliography research. By comparing the thinking patterns of big data and classical bibliography, this article draws the similarities between big data analysis and classical bibliography analysis. From a methodological perspective, both parties can take the issue as a starting point and form the final result, which is a “concept product data” cycle. Big data analysis can be simplified into three types of data analysis. For classic bibliographic data that needs to be converted, this data conversion can still refer to the big data analysis model, issue oriented, process based data analysis, and mining using classic bibliographic research methods.