Intelligent Equipment Academy, Shandong University of Science and Technology, Tai' an, Shandong, 271001, China
In recent years, China's economy and society have witnessed a considerable degree of growth and development. The industrial structure has been constantly adjusted, regional economic coordination has been continuously enhanced, and the overall economic quality has also been greatly improved. 2020 is the year of the U.S. election, and the attention of each U.S. election is undoubtedly worldwide. The world is watching to see which candidate is elected President of the United States. Because different candidates have different ideas about American policies. As the only superpower in the world, the various actions of the US will have a great or small impact on the world economy. What policies will different us candidates adopt and what impacts will they have on the US economy, finance or other fields, we need to analyze them according to the data. We quantified the relevant data of the US economy by means of quantitative methods such as statistical accumulation, statistical calculation, percentage quantification and fractional quantification. For the data such as GDP, total fiscal expenditure, public construction expenditure, medical insurance, etc., we directly made statistics, and then quantified them by summative calculation and other methods. This paper establishes a model of factors affecting economic vitality, makes use of fuzzy comprehensive evaluation, and finally analyzes the quantitative results, showing that the election of different candidates as US President stimulates the US economy. Through the analysis of the relevant economic data of the United States in the four years before and after 2016, we assessed the impact of the economic policy changes of different candidates on the economic vitality of the United States. By comparing and analyzing the relevant economic data of China in the first four years of 2016 and the last four years of 2016, after index processing, MATLAB is used to draw a comparison, and regression analysis model data is combined to compare the influence of different candidates' election in the US on China's economic politics. We will combine the data models of question 1 and question 2, and make Suggestions on China's economic countermeasures and policies in relevant fields by using models and qualitative assessment based on the relevant data of different candidates. To solve this problem, we will combine the results and influences of the models established in the first two questions, how to increase economic competitiveness and maintain sustainable development of economic vitality. Analyze the impact of these results on various aspects of China's development and make recommendations.
US election, economic vitality, AHP, fuzzy comprehensive evaluation, MATLAB
Lixin Liu. Analysis of the U.S. economy using mathematical modeling. Academic Journal of Mathematical Sciences (2022) Vol. 3, Issue 1: 55-67. https://doi.org/10.25236/AJMS.2022.030108.
 Zhang Cong. A framework analysis of the U.S. mainstream media's coverage of Hillary's 2016 election from the perspective of elite theory [D]. Shanghai International Studies University. 2019
 Hu Yanan. Globalization and the evolution of American social structure: a study of class factors in the 2016 US election [D]. China Youth Politics Academy, 2018.
 Diao D. The peculiarities of the 2020 U.S. election and its impact [J]. Modern International Relations, 2020 (08):9-16+61.
 Liu Weidong. Experimental analysis of voter factors in the 2020 U.S. election [J]. American Studies, 2020, 34(04):68-93+6-7.
 Wang Dong, Dong Chunling, Zhang Zhaoxi, Ji Cheng, An Gang. The election results will not reverse the tone of U.S. competition-oriented China policy: a roundtable interview on "2020 U.S. elections and Sino-U.S. relations"[J]. World Knowledge, 2020(14):19-23.
 Su X, Pei HJ, Wu YY, Gao JNS, Lan XY. Application of BP neural network optimized by genetic algorithm to predict coke yield of catalytic cracking unit [J]. Chemical Progress, 2016, 35(02):389-396.
 Liu Chunyan Ling Jianchun, Kou Linyuan, Qiu Lixia, Wu Junqing. Performance comparison of GA-BP neural network and BP neural network [J]. China Health Statistics, 2013,(02):173-176+181.
 Liu HORAN, Zhao CUI. Xiang, Li Xuan,Wang YANXIA,Guo CHANGJIANG. A research on a neural network optimization algorithm based on improved genetic algorithm [J]. Journal of Instrumentation, 2016, 37(07):1573-1580.