Literature clustering method based on citation network and large language model analysis

By combining citation networks with large language models, the shortcomings of existing tools in literature clustering and review are addressed, enabling efficient and accurate identification of research directions and trend tracking, and generating highly systematic and credible literature reviews.

CN120929602APending Publication Date: 2025-11-11BEIJING TECH & BUSINESS UNIV
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Patent Information

Application Number
CN202510988129.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-17
Publication Date
2025-11-11

AI Technical Summary

Technical Problem

Existing tools struggle to automatically identify and organize multiple mainstream research directions or subfields within a single field, resulting in fragmented review content and an inability to systematically track the dynamic development of research hotspots. Furthermore, large language models are prone to generating knowledge illusions when processing large volumes of literature.

Method used

By constructing a citation network and using a large language model to perform cluster analysis on reference titles, highly cited and citing references are selected. Combined with cluster quality assessment and deep semantic analysis, structured reviews and visualization results are generated.

Benefits of technology

It improves the comprehensiveness, accuracy, and systematic nature of literature reviews, reduces human resource costs, enables rapid response to new literature data, generates high-quality, timely review content, and provides in-depth domain insights.

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Abstract

The invention relates to the technical field of literature clustering, in particular to a literature clustering method based on a citation network and large language model analysis, which comprises the following steps of: obtaining and preprocessing literatures; clustering is carried out on the reference literature questions; constructing a clustering quality evaluation mechanism; screening highly cited literatures and cited literatures; carrying out abstract analysis and panoramic scanning; and generating a structured review and visualization. According to the method, a large language model is used for carrying out clustering analysis on reference literature topics, finding out mainstream clusters, ensuring comprehensiveness and systematicness of the review content, screening out highly-introduced literatures from the mainstream clusters, ensuring high quality and authority of the review content, analyzing the abstract of the literatures by using the large language model, extracting important topics, and improving the accuracy of the review content. The accuracy and depth of the review content are ensured, and the comprehensiveness, accuracy, systematicness and efficiency of literature review can be remarkably improved.
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