Affiliations: Xinjiang Agricultural University, College of Economics and Management, Urumqi, Xinjiang, China
Correspondence:
[*]
Corresponding author: Yong Xia, Xinjiang Agricultural University, College of Economics and Management, Urumqi, Xinjiang 830001, China. E-mail: 747850185@qq.com.
Abstract: Using data mining, the purpose of this study is to forecast and analyze the growth of the cotton cultivation industry and the policy financial support demands in the Aksu region. Data mining is a method for maximizing the value of data via the application of numerous algorithms. In contrast to conventional data mining, which adheres to specific algorithms, data mining employs a variety of analysis algorithms to analyze raw data, such as image and panel data, and produce accurate results. In this paper, we propose a data mining method that combines the semantic segmentation algorithm of remote sensing images with various nonlinear regression algorithms to predict the demand for policy-based financial support in a specific region based on a combination of multiple factors, including agricultural crop cultivation area, catastrophe analyses, agricultural price and inflation rates, etc. This paper intends to estimate and analyze actual data pertaining to the cotton cultivation industry in Aksu, and this methodology can further improve the policy-based financial inverse model. The methods presented in this paper can further improve countercyclical regulation of policy finance.
Keywords: Policy finance, deep learning, data mining, agricultural economy
