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Research on Financial Early Warning Model for Papermaking Enterprise based on Particle Swarm K-means Algorithm

机译：基于粒子群K-均值算法的造纸企业财务预警模型研究

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摘要

The paper industry is a dual capital and technology intensive enterprise, which is facing the pressure of industrial transformation. Strengthening the financial management ability of papermaking enterprises is the key factor to enhance competitiveness. Currently, data mining algorithms have been used more and more widely in financial early warning. In this paper, the authors analyse the financial early warning model based on particle swarm K-means algorithm and rough set theory. The empirical results show that the financial situation of the listed companies and papermaking enterprises con be evaluated comprehensively by this method, and the accuracy ot the clustering results is further tested. The results show that the data algorithm based on the data algorithm can be used to classify the company reasonably. Therefore, data mining technology can be applied to financial early warning of listed companies, also in paper industry, and the accuracy of evaluation results is high. In addition, paper enterprises should optimize the financial information mechanism and set up special departments to manage financial information.
机译：造纸业是资本和技术密集的双重企业，面临着产业转型的压力。增强造纸企业财务管理能力是增强竞争力的关键因素。当前，数据挖掘算法已越来越广泛地用于金融预警中。本文基于粒子群K-均值算法和粗糙集理论对金融预警模型进行了分析。实证结果表明，该方法可以对上市公司和造纸企业的财务状况进行综合评价，并对聚类结果的准确性进行检验。结果表明，基于数据算法的数据算法可以合理地对公司进行分类。因此，数据挖掘技术可以应用于造纸行业的上市公司财务预警，评估结果的准确性也很高。此外，纸业企业应优化财务信息机制，并设立专门部门管理财务信息。

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