Affiliations: [a] The Library, Hebei University of Engineering, Handan 056038, China | [b] Logistics Management Office, Hebei University of Engineering, Handan 056038, China
Abstract: In order to overcome the problems of retrieval accuracy and time-consuming of traditional document information retrieval methods, this paper designs an intelligent retrieval method of library document information based on hidden topic mining. Firstly, LDA model is used to mine the hidden topics of library document information, and then, based on the mining results, similarity degree of document information is calculated in inference network model. Finally, the Bayesian model is constructed in the sample space to retrieve the library literature information under the maximum retrieval space coverage. Experimental results show that, compared with traditional retrieval methods, the proposed method improves the retrieval accuracy significantly, with the highest retrieval accuracy reaching 99%, and the retrieval time is significantly reduced, indicating that the proposed method effectively improves the retrieval accuracy and timeliness.
