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Farmer’s Credit Rating Model and Application Based on Multilayer Unified Network with Linear Classifier

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  • Sulin Pang
  • Shouyang Wang
  • Lianhu Xia

Abstract

This article innovatively builds the infrastructure of farmer credit rating index system into a multilevel unidirectional network structure. First, according to the logical structure of the three-level credit rating index system, a four-level unidirectional network is constructed, and the credit rating calculation formulas of all indexes at the four-level network are established. Furthermore, the special cases of the credit rating formula with the first- and second-level farmer credit rating index system are discussed. On this basis, it is extended to a credit rating index system with more than four levels, and the corresponding credit rating formula is established. Finally, the general formula of credit rating formula of the farmer credit rating index system from first level to multilevel is obtained. In order to solve the problem of farmers' credit rating, this paper also designs a linear segmentation classifier to classify the results of multilayer unidirectional network, establishes the rules of farmers' credit rating and the unidirectional network linear segmentation evaluation model of farmers' credit rating, and discusses the properties of bank credit based on farmers’ credit rating. Finally, the model established in this paper is applied to the credit rating of farmers in County, Guangdong Province in China. When the credit rating of 160 farmers is carried out, the evaluation results are in line with the actual credit rating of farmers in County, with an accuracy of 100%. This research has the maneuverability to carry on the scientific credit rating to the countryside. This study has important method guidance and operability for rural credit rating.

Suggested Citation

  • Sulin Pang & Shouyang Wang & Lianhu Xia, 2020. "Farmer’s Credit Rating Model and Application Based on Multilayer Unified Network with Linear Classifier," Complexity, Hindawi, vol. 2020, pages 1-13, October.
  • Handle: RePEc:hin:complx:7096952
    DOI: 10.1155/2020/7096952
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