Proceedings of the 2021 International Conference on Modern Management and Education Research (MMER 2021)

Research on the Neural Network Model Forecast of Biden Administration’s China Policy

Authors
Zhijun Guo, Lu Cheng
Corresponding Author
Zhijun Guo
Available Online 16 September 2021.
DOI
10.2991/assehr.k.210915.018How to use a DOI?
Keywords
Neural network model, Economic impact, Forecast, Coping strategy
Abstract

The direction of Sino-US relations not only affects the fundamental interests of the two peoples but also has an important impact on world peace and stability. In this paper, by studying a series of policies and systems respected by the Democratic Party represented by Biden, and applying the neural network model to analyze its policies, further analyze the influence of the Biden administration on the Biden Administration’s Impact on China’s Economy, propose corresponding strategies on financial and trade, immigration, capital markets, environmental protection, and military issues.

Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2021 International Conference on Modern Management and Education Research (MMER 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
16 September 2021
ISBN
10.2991/assehr.k.210915.018
ISSN
2352-5398
DOI
10.2991/assehr.k.210915.018How to use a DOI?
Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhijun Guo
AU  - Lu Cheng
PY  - 2021
DA  - 2021/09/16
TI  - Research on the Neural Network Model Forecast of Biden Administration’s China Policy
BT  - Proceedings of the 2021 International Conference on Modern Management and Education Research (MMER 2021)
PB  - Atlantis Press
SP  - 76
EP  - 79
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.210915.018
DO  - 10.2991/assehr.k.210915.018
ID  - Guo2021
ER  -