[目的/意義]大數據環境下,多源情報的粗糙性與多學科、跨領域性,以及單源情報片段的片面性與模糊隨機不確定性,為競爭情報的收集與分析帶來挑戰。[方法/過程]文章提出了一種基于情報元相似度的多源競爭情報片段融合方法,通過四次相似度分析過程,實現情報元的序化、融合以及重構,消除冗余和噪音信息的同時,解決了情報碎片的內容整合問題,為競爭情報的深度知識融合提供堅實的數據支撐。[結果/結論]針對企業A的實例研究,實現了多源情報片段產品情報元的相似度分析與融合,一定程度上體現了本方法的科學性、可行性和智能性。[局限]針對海量情報元的相似度算法待進一步優化。

[Purpose/significance] In the big data environment,the roughness,multidisciplinary and cross-domain characteristicsof multi-source intelligence,as well as the one-sidedness and fuzzy random uncertainty of single source intelligence fragments,bring challenges for the collection and analysis of competitive intelligence. [Method/process] This paper proposes an approach of multi-source competitive intelligence fragments fusion based on intelligence element similarity,which aims to eliminate the redundant and interferential information and solve the content integration problem of intelligence fragments through four processes of similarity analysis as well as the sequencing,integration and reconstruction strategies of intelligence element,which can provide a solid data support for the in-depth knowledge fusion of competitive intelligence. [Result/conclusion] A fusion case study of the enterprise A realizes the similarity analysis and fusion process of the product intelligence elements of multi-source intelligence fragments,which reflects the scientificity,feasibility and intelligence of this method to a certain extent. [Limitations]Similarity algorithm optimization of mass intelligence elements needs to be further carried out.