Analysis of the Path of Utilizing Big Data to Innovate Archive Management Mode to Enhance Service Capability

Abstract

With the rapid development of modern science and technology, we are now in an era of big data and digitalization of network, and people’s normal work and life are also implicitly influenced. Archives, as the information of various work examinations, are the imprints of the past and the basis for guiding the future. Compared with the traditional management, the information-based archive management is more efficient and of better quality. The efficiency of all sectors of society in the background of the information age has taken a qualitative leap and improved. In order to develop the archive management business and make the archive management more efficient and secure, a set of scientific and efficient information-based archive management work mode should be worked out on the road of practice in the new era. With the arrival of the era of big data brings fundamental changes to data mining technology, which can analyze and process a large amount of data and dig intelligently, making it possible to dig deeper into the data. Therefore, it is necessary to actively explore archival data mining technology, so that data mining technology can be widely applied to archival management work, and the potential archival information can be excavated and delivered as much as possible to play its proper role and maximize the economic and social benefits of archival data. The article analyzes the characteristics of archive management in the context of big data, proposes a specific path to improve the quality of archive management work in view of the current problems in archive management work, and designs the archive management structure based on big data for improving the quality of archive management work.

Application of Artificial Intelligence-based Technology in College Archives Management

“…Furthermore, W. Luo and H. Hu [15]. Studies have looked into the use of machine learning methods like as Support Vector Machines (SVMs) and Random Forests in archival classification and information retrieval tasks.…”