跨境电商物流绩效评价研究  

Research on Cross-Border E-Commerce Logistics Performance Evaluation

作　　者：耿晓芬[1] GENG Xiaofen(Chengdu Institute Sichuan International Studies University,Chengdu 611844,China)

机构地区：[1]四川外国语大学成都学院,四川成都611844

出　　处：《物流科技》2022年第16期50-53,共4页Logistics Sci-Tech

基　　金：2021—2023年高等教育人才培养质量和教学改革项目“服务川渝双城经济圈多语种跨境电商基地建设与人才培养模式构建”(JG2021-1537)。

摘　　要：文章以跨境电子商务的国际贸易绩效评价为研究对象,基于深度神经网络模型,开发跨境国际贸易绩效的评价模型,以改变贸易策略,提高贸易绩效。首先,分析了各种神经网络模型,如人工神经网络、“BP”神经元模型和LSTM神经网络。其次,总结了一个关于跨境电商发展的深度神经网络模型,指出了目前跨境电商国际贸易绩效评价中存在的问题:一是电子商务市场监管体系不健全;二是评价指标不一致;三是评价体系不完善。最后,针对跨境电商国际贸易绩效评价提出相关的建议,指出各种神经网络的优缺点,以及它们在跨境电商绩效评价中的作用,并通过实验比较这些神经网络。The paper takes the international trade performance evaluation of cross-border e-commerce as the research object,and develops the evaluation model of cross-border international trade performance to change trade strategies and improve trade performance based on the deep neural network model.The paper firstly analyzes various neural network models,such as artificial neural network“,BP”neuron model,and LSTM neural network.Secondly,it summarizes a deep neural network model that is beneficial to the development of cross-border e-commerce,and points out the problems of the current international cross-border e-commerce trade performance evaluation:firstly,e-commerce market regulatory system is not sound and perfect;secondly,the evaluation index is inconsistent;thirdly,the evaluation system is not perfect.Finally,the paper puts forward relevant suggestions on the performance evaluation of cross-border e-commerce international trade,points out the advantages and disadvantages of various neural networks,and their roles in cross-border e-commerce performance evaluation,and compares these neural networks through experiments.

关 键 词：B2C业务 物流网络优化 神经元网络模型 

分 类 号：F259.23[经济管理—国民经济]