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Research on the Innovation of Cross-border E-commerce Enterprise Management Mode Based on Big Data Analysis

模式(计算机接口) 大数据 业务 知识管理 数据科学 过程管理 计算机科学 数据挖掘 操作系统
作者
Ruixue Ma
出处
期刊:Applied mathematics and nonlinear sciences [De Gruyter]
卷期号:9 (1)
标识
DOI:10.2478/amns-2024-3292
摘要

Abstract With the rapid development of information science and technology, enterprise management gradually began to rely on information science and technology, the change of management mode to promote the development of the enterprise economy. This paper focuses on innovation in cross-border e-commerce enterprise management mode to conduct the main research. The article first puts forward the cross-border e-commerce management innovation mode based on big data analysis and then uses the data mining method to analyze the funds and economic data of each subsidiary of the e-commerce enterprise and discovers that all kinds of dimensional data correlation, based on the clustering results, found that the relationship between enterprise orders and enterprise earnings is not positively correlated, and the relationship between earnings The relationship between total amount and social accumulation rate is positively correlated, and so on. Subsequently, we consider cross-border e-commerce enterprises as the research object. Based on the K-Means algorithm and Apriori algorithm, we use a Python tool to carry out association rule mining and clustering analysis on the company’s sales data and then visualize the results of the analysis: among the 30 agents of enterprise A, there are 2 very important agents, accounting for 4.22%. The number of important agents is 15, which accounts for 48.33%, and the number of general agents is 11, which accounts for 39.88%. Using the management model proposed in this paper can help agents with poor sales improve customer adhesion, and can provide effective decision-making assistance for managers.

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