Research on Library Bibliographic Information Integration Technology Combined with Group Intelligence

Abstract

Abstract There are abundant bibliographic resources in libraries, but the resource search platforms and search methods are different, which brings difficulties to readers in using and causes problems such as low resource utilization. In this paper, the search performance of the QPSO algorithm is improved by using reverse learning and dynamic group search method to get the QPSO-DGS algorithm, and then the MSP strategy is introduced to synthesize the MSP-QPSO-DGS algorithm, which realizes the parallelization of group intelligence algorithm in the integration of bibliographic information. Subsequently, a system for integrating bibliographic information in a library is constructed using the MSP-QPSO-DGS algorithm. According to the performance test, the MSP-QPSO-DGS algorithm suggested in this paper has a superior convergence accuracy than the optimization results of other population intelligence algorithms. Meanwhile, it is found that the system proposed in this paper has an average checking completeness rate and checking accuracy rate of 93.01% and 90.85% in bibliographic information integration, respectively, and has achieved good results in practical applications. This paper has made meaningful attempts and explored to provide efficient and accurate library bibliographic information integration techniques, and the proposed system provides a reference for research in this field.