Research on the Forecast of Jiangxi's Digital Economy Scale Based on EWM and BP-Neural Network Models

Linbo Chen, Zhining Zhang, Yu Zhao

2022

Abstract

Accurately judging the future development trend of the digital economy is the premise of exploring the realization path of strengthening the digital economy of Jiangxi. We use EWM model to measure the digital economy development index of Jiangxi Province from 2011 to 2020. Secondly, we build a predictive model for the scale of Jiangxi's digital economy based on BP neural network method. Finally, we use scenario analysis and the prediction model to simulate the development path and predict the scale of Jiangxi's digital economy. The results show that: First, during 2011-2020, the scale of digital economy development in Jiangxi Province has grown steadily, with the fastest month-on-month growth rate of 46.29% in 2017. Secondly, the independent variables selected in this paper can positively affect the development of the digital economy in Jiangxi Province. The correlation between residents' wage level and digital economy scale index is the strongest. Third, the scale of Jiangxi's digital economy can achieve sustainable growth on the premise of maintaining the current development trend or changing the development path during 2021-2025, and the digital economy gains is the most significant under the development mode of path 4.

Paper Citation

in Harvard Style

Chen L., Zhang Z. and Zhao Y. (2022). Research on the Forecast of Jiangxi's Digital Economy Scale Based on EWM and BP-Neural Network Models. In Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME; ISBN 978-989-758-636-1, SciTePress, pages 562-569. DOI: 10.5220/0012036900003620

in Bibtex Style

@conference{icemme22,
author={Linbo Chen and Zhining Zhang and Yu Zhao},
title={Research on the Forecast of Jiangxi's Digital Economy Scale Based on EWM and BP-Neural Network Models},
booktitle={Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME},
year={2022},
pages={562-569},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012036900003620},
isbn={978-989-758-636-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME
TI - Research on the Forecast of Jiangxi's Digital Economy Scale Based on EWM and BP-Neural Network Models
SN - 978-989-758-636-1
AU - Chen L.
AU - Zhang Z.
AU - Zhao Y.
PY - 2022
SP - 562
EP - 569
DO - 10.5220/0012036900003620
PB - SciTePress