大数据背景下的电子商务信用风险预警研究  

Research on E-commerce Credit Risk Early Warning Under the Background of Big Data

作　　者：孙媛[1] SUN Yuan(School of Big Data and Artificial Intelligence,Dalian University of Finance and Economics,Dalian Liaoning 116622,China)

机构地区：[1]大连财经学院大数据与人工智能学院,辽宁大连116622

出　　处：《佳木斯大学学报（自然科学版）》2024年第4期52-55,共4页Journal of Jiamusi University：Natural Science Edition

基　　金：辽宁省教育厅2022年度高校基本科研项目(LJKMR20221958)。

摘　　要：为了提高电子商务信用风险预警效果,设计了一种大数据背景下的电子商务信用风险预警方法。首先设计电子商务信用风险预警指标,并采集大量的电子商务信用风险数据,然后分别采用最小二乘支持向量机和神经网络对电子商务信用风险预警进行建模,并对它们预警结果进行组合,最后与传统方法进行电子商务信用风险预警对比分析,分析结果表明设计的方法可以准确找到电子商务信用风险预警变化特点,电子商务信用风险预警正确率超过94%,远远高于传统方法的电子商务信用风险预警结果,大幅度降低了电子商务信用风险的误警率,具有十分明显的优越性。In order to improve the effect of e-commerce credit risk early warning,An e-commerce credit risk early warning method under the background of big data is designed.Firstly,the e-commerce credit risk early warning index is designed,and a large number of e-commerce credit risk data are collected.Then,the e-commerce credit risk early warning is modeled by support vector machine and neural network respectively,and their early warning results are combined.Finally,compared with the traditional methods,this method can accurately find the change characteristics of e-commerce credit risk early warning.The accuracy of e-commerce credit risk early warning is more than 94%,which is much higher than the e-commerce credit risk early warning results of the traditional methods,and greatly reduces the false alarm rate of e-commerce credit risk,It has obvious advantages.

关 键 词：大数据背景 电子商务 信用风险 预警正确率 仿真测试 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]