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Chinese Journal of Management Science ›› 2022, Vol. 30 ›› Issue (12): 234-244.doi: 10.16381/j.cnki.issn1003-207x.2021.2648

• Articles • Previous Articles    

The Framework for Characteristic Factors of Poverty Statusby Using AI Algorithms:Related to the Path Choice of Rural Revitalization in China

YUAN George Xianzhi1, 2, 3, 4, 5, 6, ZHAO Min7, LIU Hai-yang6, ZHOU Yun-peng6, YAN Cheng-xing6, SHI Bao-feng8, CHAI Na-na8, LIN Jian-wu2, HE Cheng-ying2, MA Sheng1, ZHANG Qian-you1, DING Xiao-wei9   

  1. 1. Business School, Chengdu University, Chengdu 610106;2. Saxo Fintech Business School, University of Sanya, Hainan, 572000;3. Business School, Sun Yatsen University, Guangdong 510275;4. School of Science, Chongqing University of Technology, Chongqing 400054;5. Business School, East China University of Science and Technology, Shanghai 200237;6. Shanghai Hammer Digital Technology, LtD.Hammer, Shanghai 200093;7. Information Center, Yunnan Tobacco Company, Kunming 650000;8. College of Economics and Management, Northwest A&F University, Xianyang 712100;9. School of Information Management & Inclusive & Rural Fintech Innovation Research Center, Nanjing University, Nanjing 220023, China
  • Received:2021-04-20 Revised:2022-01-14 Published:2023-01-10
  • Contact: 张千友 E-mail:zhangqianyou@163.com

Abstract: The goal of this paper is to establish a framework and associated analysis process for the extraction of related features to depict the poverty status of rural farmers. Based on the 18 types of data covered by a rural filing cardin China, combined with CART analysis and Gibbs sampling algorithm, 12 highly related characteristic factors are screened out to describe the poverty status of rural households.

Key words: machine learning algorithm; gibbs sampling; family farm; credit rating; rural vitalization; poverty state; associated feature

CLC Number: