Research on Literature Clustering Algorithm for Massive Scientific and Technical Literature Query Service

Abstract

Traditional science and technology literature search mainly provides users with reliable and detailed information materials and services through technical means, data resources, and service strategies. With the development of network technology, computer technology, and information technology, digital information resources are increasing day by day, which continuously impact the traditional knowledge service mode. Some traditional technical methods and service means can no longer meet the information needs of users under large data sets. This paper proposes a model of large-scale literature search service in the context of big data by studying the technical means and service modes used for scientific and technical literature search in universities in the era of big data. Specifically, this paper proposes a method for fast literature retrieval by combining R-tree indexing for the characteristics of diverse data types and large data volume of science and technology literature. The method uses an improved k-mean clustering algorithm to construct an R-tree clustering model and improve the retrieval efficiency of the system by retrieving scientific and technical literature data through R-tree indexing. Experiments on university science and technology literature datasets show that the method in this paper improves both efficiency and precision when searching literature.