<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The innovation of international trade methods is the business of e-commerce and network trade as the transaction method. In this paper, based on the historical data of China’s foreign trade import and export, combined with the data characteristics of its information, the predictive analysis of the effect of China’s international trade methods after innovation is proposed using the support vector regression model. In the prediction analysis, the support vector regression model is mainly used to model the prediction of China’s foreign trade import and export information in time series. Finally, the model was tested using real data from the foreign trade import and export information system, and examples of different types of support vector machines, kernel functions, and the selection of their parameters were discussed. The results show that the total import and export of China’s goods trade reached RMB 30.65 trillion in 2018, and the global share of goods import and export reached 11.9%. The share of cross-border e-commerce transactions in total imports and exports increased from 7.76% in 2011 to 27.2% in 2016. The study shows that cross-border e-commerce among new trade modes has become a driving force for China’s foreign trade growth and a new growth point and engine for developing China’s foreign trade.</p>
</abstract>

The innovation of international trade methods is the business of e-commerce and network trade as the transaction method. In this paper, based on the historical data of China’s foreign trade import and export, combined with the data characteristics of its information, the predictive analysis of the effect of China’s international trade methods after innovation is proposed using the support vector regression model. In the prediction analysis, the support vector regression model is mainly used to model the prediction of China’s foreign trade import and export information in time series. Finally, the model was tested using real data from the foreign trade import and export information system, and examples of different types of support vector machines, kernel functions, and the selection of their parameters were discussed. The results show that the total import and export of China’s goods trade reached RMB 30.65 trillion in 2018, and the global share of goods import and export reached 11.9%. The share of cross-border e-commerce transactions in total imports and exports increased from 7.76% in 2011 to 27.2% in 2016. The study shows that cross-border e-commerce among new trade modes has become a driving force for China’s foreign trade growth and a new growth point and engine for developing China’s foreign trade.

Innovation of China’s international trade methods based on the perspective of cross-border e-commerce

Hainan Vocational University of Science and Technology

Applied Mathematics and Nonlinear Sciences

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

The innovation of international trade methods is the business of e-commerce and network trade as the transaction method. In this paper, based on the historical...

Innovation of China’s international trade methods based on the perspective of cross-border e-commerce

Online veröffentlicht: 18. Aug. 2023

Seitenbereich: -

Eingereicht: 30. Sept. 2022

Akzeptiert: 27. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00205

Schlüsselwörter
Cross-border e-commerce, Data characteristics, Support vector machine, Time series, Kernel function.

© 2023 Yu Zhai, published by Sciendo

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

Innovation of China’s international trade methods based on the perspective of cross-border e-commerce

Online veröffentlicht: 18. Aug. 2023

Seitenbereich: -

Eingereicht: 30. Sept. 2022

Akzeptiert: 27. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00205

SchlüsselwörterCross-border e-commerce, Data characteristics, Support vector machine, Time series, Kernel function.

© 2023 Yu Zhai, published by Sciendo

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

Schlüsselwörter
Cross-border e-commerce, Data characteristics, Support vector machine, Time series, Kernel function.