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

<p>In this paper, we first study data-based e-commerce operation from the big data perspective and elaborate on data-based e-commerce operation from four aspects: professional terminology, operation core, theoretical operation basis, and operation method. Secondly, in the process of e-commerce data-based operation sales prediction, data pre-processing and feature selection are required, and the quality of data and features directly determines the accuracy of the model and based on deep learning, the structure of convolutional and XG fusion prediction model is proposed. Then, three-dimensional data frames are constructed based on four-dimensional data facets, which leads to more accurate model predictiveness, and the prediction model and e-commerce financial and operational risks are studied and analyzed. The results show that the convolutional and XG fusion forecasting model has a greater efficiency enhancement function than the traditional data computation model in the practical application of the forecasting model, proving the model’s stability in the long-term forecasting process. By predicting risk in e-commerce operations in time and improving it in real-time, this study can help the enterprise develop more competitively.</p>
</abstract>

In this paper, we first study data-based e-commerce operation from the big data perspective and elaborate on data-based e-commerce operation from four aspects: professional terminology, operation core, theoretical operation basis, and operation method. Secondly, in the process of e-commerce data-based operation sales prediction, data pre-processing and feature selection are required, and the quality of data and features directly determines the accuracy of the model and based on deep learning, the structure of convolutional and XG fusion prediction model is proposed. Then, three-dimensional data frames are constructed based on four-dimensional data facets, which leads to more accurate model predictiveness, and the prediction model and e-commerce financial and operational risks are studied and analyzed. The results show that the convolutional and XG fusion forecasting model has a greater efficiency enhancement function than the traditional data computation model in the practical application of the forecasting model, proving the model’s stability in the long-term forecasting process. By predicting risk in e-commerce operations in time and improving it in real-time, this study can help the enterprise develop more competitively.

Feasibility study of deep learning based on data-based e-commerce operations

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Feasibility study of deep learning based on data-based e-commerce operations"}

In this paper, we first study data-based e-commerce operation from the big data perspective and elaborate on data-based e-commerce operation from four aspects:...

Feasibility study of deep learning based on data-based e-commerce operations

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 11 nov 2022

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00880

Parole chiave
Deep learning, Convolutional and XG fusion, Predictive models, E-commerce, Data-enabled operations

© 2023 Haohao Yue, published by Sciendo

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

Feasibility study of deep learning based on data-based e-commerce operations

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 11 nov 2022

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00880

Parole chiaveDeep learning, Convolutional and XG fusion, Predictive models, E-commerce, Data-enabled operations

© 2023 Haohao Yue, published by Sciendo

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

Parole chiave
Deep learning, Convolutional and XG fusion, Predictive models, E-commerce, Data-enabled operations