The evaluation of agricultural enterprise's innovative borrowing capacity based on deep learning and BP neural network

Yingli Wu () and Guangji Tong ()
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Yingli Wu: Northeast Forestry University
Guangji Tong: Northeast Forestry University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 3, No 15, 1123 pages

Abstract: Abstract The purpose is to solve the financing difficulties of agricultural enterprises with the assistance of deep learning and neural network model, improve the overall borrowing ability of enterprises and effectively evaluate enterprise risks. First, the concepts of deep learning and neural network are introduced. On the basis of sorting out and summarizing the existing relevant research, multiple financial indexes are selected as variables, and a risk control model of innovative lending of agricultural enterprises based on back propagation neural network (BPNN) is established. The data of some listed agricultural enterprises are selected for empirical analysis. The performance of the proposed model algorithm is compared with that of the previous model. The results show that the evaluation error of BPNN algorithm is less than 1.8%, and its accuracy is as high as 94.04%. The algorithm shows excellent performance in the training process, the actual output value is very close to the expected value, and can effectively classify related enterprises. The research results can provide reference for solving the loan problem of agricultural enterprises and improving the risk management ability of agricultural enterprises.

Keywords: Deep learning; BP neural network; Innovative agricultural enterprise; Borrowing capacity; Maximum and minimum method (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01462-8

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