基于E-KNN算法的企业数据分级分类管理方法研究  

Research on Enterprise Data Hierarchical Classification Management Method Based on E-KNN Algorithm

作　　者：吕晓英 LV Xiao-ying(Hefei Information Technology Vocational College,Anhui,Hefei 230601)

机构地区：[1]合肥信息技术职业学院,安徽合肥230601

出　　处：《贵阳学院学报(自然科学版)》2025年第1期70-74,共5页Journal of Guiyang University:Natural Sciences

基　　金：2023年安徽省高等学校科学研究项目(哲学社会科学)重点项目“基于国密和双向链锁算法的智慧农业云平台端到端加密系统研究与设计”(2023AH052504)。

摘　　要：针对现阶段企业数据管理方法存在的准确率低下与分级分类管理难题,采用指数函数距离加权与因子分析方法对K最近邻算法进行了改进,提出了一种新型的企业数据分级分类管理方法。实验结果表明,通过加入10-折交叉验证与网格搜索,K最近邻算法在训练集和测试集的自动分级分类精确度分别能够达到85.9%与85.3%。进一步通过指数函数距离加权与因子分析方法进行改进,K最近邻算法在训练集和测试集的分级分类预测准确率更是分别达到了95.1%与95.4%。由此可知,所提出的改进方法能够显著提高企业数据管理的效率和精度,对于推动企业数据治理向更高水平发展具有重要意义。Aiming at the low accuracy and hierarchical classification management difficulties existing in the current stage of enterprise data management methods,the study improves the K nearest neighbour algorithm by means of exponential function distance weighting and factor analysis methods,and finally proposes a new hierarchical classification management method for enterprise data.The experimental results show that by adding 10-fold cross-validation and grid search,the K nearest neighbour algorithm is able to achieve 85.9%and 85.3%of automatic hierarchical classification accuracy in the training set and test set,respectively.Further improved by the exponential function distance weighting and factor analysis methods,the K nearest neighbour algorithm is able to reach 95.1%and 95.4%of the prediction accuracy of hierarchical classification in the training set and test set,respectively.It can be seen that the proposed improvement method can significantly improve the efficiency and accuracy of enterprise data management,which is of great significance to promote the development of enterprise data governance to a higher level.

关 键 词：K最近邻算法 因子分析法 企业数据 分级分类 管理 

分 类 号：F406.7[经济管理—产业经济]