基于产业链图谱的服装企业产业链挂链关系挖掘OA北大核心CSTPCD

The construction of the clothing industry chain map has become a focal area and a key strategy for the digital upgrade of China's clothing industry.Serving as a vital tool,the clothing industry chain map helps enterprises and researchers better understand and grasp the structure,relationships,and dynamics of the entire industry chain. This study aims to use digital technology to visually present the entire clothing industry chain,so as to enhance the efficiency,reduce costs,optimize the resource allocation,and ultimately boost the competitiveness and market position of enterprises.In the clothing industry chain map,determining which industry chain point a company belongs to and understanding the relationships among various enterprises are crucial for industry investment,resource optimization,production efficiency improvement,and cost reduction.However,traditional methods of enterprise linkage often involve manual examination of company names,business scopes,and product information,leading to time-consuming and inefficient processes with suboptimal results.Therefore,researching automatic linkage algorithms for clothing enterprises is of practical significance and theoretical value in optimizing the clothing industry chain map.Current research efforts are primarily focused on text information mining and machine learning methods.Nevertheless,limited research has been conducted on how to use the enterprise profiles and industrial chain map for automatic linkage in the industry chain.This study addresses this gap by collecting enterprise information,extracting keywords,establishing an enterprise information database,and proposing an automatic linkage algorithm based on the CoSENT model.The algorithm utilizes the CoSENT model to calculate the similarity between enterprise keywords and industry chain points,filters matching results through custom rules,assesses the relevance between keywords and points,and achieves automatic linkage in the industry chain for enterprises.Leveraging machine learning technology,this approach provides a more feasible solution for handling vast amounts of information related to clothing enterprises. Experimental results demonstrate that the proposed algorithm significantly outperforms other traditional algorithms on the F1-Measure metric.Compared to the Jaccard method,the accuracy of this algorithm improves by 14%;compared to the Word2Vec method,it improves by 10.5%;and compared to the SBERT method,it improves by 2.5%.The substantial enhancement elevates the accuracy and efficiency of enterprise linkage,providing robust support and guidance for optimizing the clothing industry chain map.Future research directions include collecting more enterprise information to build richer enterprise profiles,so as to further enhance the linkage efficiency.This study offers a practical solution for the digital upgrade and optimization of the clothing industry chain.