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

<p>For the third-party cross-border e-commerce sharing platform, due to the lack of management and operation mechanism, the current sharing platform has relatively high fees, insufficient publicity, and consumption methods are not conducive to Chinese consumers. There are obvious problems such as insufficient personnel training. In this article, we use the CGA-LSO-BP network to solve the problems of various systems disjoint, complicated department settings, confusing distribution of powers and responsibilities in the training base, as well as unclear division of team functions, technology mismatch, and the training base itself. The scale, social reputation and other factors lead to the difficulty of reducing financing channels and other related operation and management issues to study and analyze. The results show that the minimum error can reach 0.5% for CGA-LSO-BP, which is much smaller than the traditional algorithm. It is proved that the algorithm can help the BP neural network to jump out of the local optimal value to a certain extent, and play an active role in the regression task. In addition, the improved CGA-LSO-BP neural network based on this can provide a good reference for various problems in cross-border e-commerce, such as disjointed systems, complicated department settings, and chaotic distribution of rights and responsibilities, and propose optimal solutions.</p>
</abstract>

For the third-party cross-border e-commerce sharing platform, due to the lack of management and operation mechanism, the current sharing platform has relatively high fees, insufficient publicity, and consumption methods are not conducive to Chinese consumers. There are obvious problems such as insufficient personnel training. In this article, we use the CGA-LSO-BP network to solve the problems of various systems disjoint, complicated department settings, confusing distribution of powers and responsibilities in the training base, as well as unclear division of team functions, technology mismatch, and the training base itself. The scale, social reputation and other factors lead to the difficulty of reducing financing channels and other related operation and management issues to study and analyze. The results show that the minimum error can reach 0.5% for CGA-LSO-BP, which is much smaller than the traditional algorithm. It is proved that the algorithm can help the BP neural network to jump out of the local optimal value to a certain extent, and play an active role in the regression task. In addition, the improved CGA-LSO-BP neural network based on this can provide a good reference for various problems in cross-border e-commerce, such as disjointed systems, complicated department settings, and chaotic distribution of rights and responsibilities, and propose optimal solutions.

Research on the operation and management mechanism of cross-border e-commerce shared practice training base

Applied Mathematics and Nonlinear Sciences

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

For the third-party cross-border e-commerce sharing platform, due to the lack of management and operation mechanism, the current sharing platform has...

Research on the operation and management mechanism of cross-border e-commerce shared practice training base

Published Online: Apr 28, 2023

Page range: -

Received: Apr 05, 2022

Accepted: Jul 19, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00073

Keywords
CGA-LSO-BP network, Cross-border e-commerce, Sharing platform

© 2023 Wenwen Meng, published by Sciendo

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

Research on the operation and management mechanism of cross-border e-commerce shared practice training base

Published Online: Apr 28, 2023

Page range: -

Received: Apr 05, 2022

Accepted: Jul 19, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00073

KeywordsCGA-LSO-BP network, Cross-border e-commerce, Sharing platform

© 2023 Wenwen Meng, published by Sciendo

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

Keywords
CGA-LSO-BP network, Cross-border e-commerce, Sharing platform