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Sustainable Reuse Strategies of Enterprise Financial Management Model Following Deep Learning Under Big Data

Sustainable Reuse Strategies of Enterprise Financial Management Model Following Deep Learning Under Big Data

Na Ta, Bo Gao
Copyright: © 2022 |Volume: 34 |Issue: 8 |Pages: 18
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781668462638|DOI: 10.4018/JOEUC.300761
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MLA

Ta, Na, and Bo Gao. "Sustainable Reuse Strategies of Enterprise Financial Management Model Following Deep Learning Under Big Data." JOEUC vol.34, no.8 2022: pp.1-18. http://doi.org/10.4018/JOEUC.300761

APA

Ta, N. & Gao, B. (2022). Sustainable Reuse Strategies of Enterprise Financial Management Model Following Deep Learning Under Big Data. Journal of Organizational and End User Computing (JOEUC), 34(8), 1-18. http://doi.org/10.4018/JOEUC.300761

Chicago

Ta, Na, and Bo Gao. "Sustainable Reuse Strategies of Enterprise Financial Management Model Following Deep Learning Under Big Data," Journal of Organizational and End User Computing (JOEUC) 34, no.8: 1-18. http://doi.org/10.4018/JOEUC.300761

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Abstract

The study aims to help enterprises to formulate a financial sustainable development strategy. A financial crisis forecast system based on deep learning (DL) is proposed to assist enterprises in checking their financial bills in time, knowing about their financial situations, formulating corresponding strategies, and realizing financially sustainable development. First, the relevant theories of financially sustainable development and DL are reviewed. Second, a long short-term memory (LSTM) neural network model based on DL is implemented and the normal sample data are compared with the unbalanced sample data. Finally, the performance of the model is analyzed according to the experimental results. The experiments show that the performance of the financial crisis forecast system is the best when the time step is T-3. The accuracy rate of the LSTM model is more than 93%, and the highest value of AUC (area under the curve) is 93.67%. The AUC value of the LSTM neural network model is compared with that of the fully connected neural network model and logistic regression model.