<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>As a typical ecology of “Internet+”, cross-border e-commerce enterprises have a huge amount of data, but it is difficult to use it effectively. This paper researches the generation and development of cross-border e-commerce big data, proposes the association rule algorithm based on Apriori to mine and analyze the cross-border e-commerce data, improves the problem of high time cost of Apriori algorithm, optimizes the clustering analysis method of quantitative attributes, and adopts the distance-based quantitative association rule to search for frequent classes, and analyzes the big data of cross-border e-commerce platforms based on the improved algorithm. Big data of cross-border e-commerce platform. Based on the analysis of the high demand of the cross-border e-commerce platform based on big data, the prediction error rate is 1.75%, 2.27%, 5.48%, 2.49%, 2.91%, 2.08%, 10.18% and 1.81% in order. In terms of user portraits, the accuracy of big data analysis of users’ purchasing power, purchasing habits and consumption intentions reached 86.10%, 73.43% and 90.48% on average. Big data technology helps cross-border e-commerce companies optimize the industry chain, improve operational management efficiency, enhance consumer experience and establish a brand effect.</p>
</abstract>

As a typical ecology of “Internet+”, cross-border e-commerce enterprises have a huge amount of data, but it is difficult to use it effectively. This paper researches the generation and development of cross-border e-commerce big data, proposes the association rule algorithm based on Apriori to mine and analyze the cross-border e-commerce data, improves the problem of high time cost of Apriori algorithm, optimizes the clustering analysis method of quantitative attributes, and adopts the distance-based quantitative association rule to search for frequent classes, and analyzes the big data of cross-border e-commerce platforms based on the improved algorithm. Big data of cross-border e-commerce platform. Based on the analysis of the high demand of the cross-border e-commerce platform based on big data, the prediction error rate is 1.75%, 2.27%, 5.48%, 2.49%, 2.91%, 2.08%, 10.18% and 1.81% in order. In terms of user portraits, the accuracy of big data analysis of users’ purchasing power, purchasing habits and consumption intentions reached 86.10%, 73.43% and 90.48% on average. Big data technology helps cross-border e-commerce companies optimize the industry chain, improve operational management efficiency, enhance consumer experience and establish a brand effect.

The Application of Big Data Technology in the Efficient Development of Cross-Border E-Commerce Industry

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

As a typical ecology of “Internet+”, cross-border e-commerce enterprises have a huge amount of data, but it is difficult to use it effectively. This paper...

The Application of Big Data Technology in the Efficient Development of Cross-Border E-Commerce Industry

Online veröffentlicht: 04. Sept. 2023

Seitenbereich: -

Eingereicht: 03. Nov. 2022

Akzeptiert: 15. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00286

Schlüsselwörter
Big data technology, Cross-border e-commerce, Improved Apriori algorithm, Demand, User profiling

© 2023 Qianqian Gao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Application of Big Data Technology in the Efficient Development of Cross-Border E-Commerce Industry

Online veröffentlicht: 04. Sept. 2023

Seitenbereich: -

Eingereicht: 03. Nov. 2022

Akzeptiert: 15. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00286

SchlüsselwörterBig data technology, Cross-border e-commerce, Improved Apriori algorithm, Demand, User profiling

© 2023 Qianqian Gao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data technology, Cross-border e-commerce, Improved Apriori algorithm, Demand, User profiling