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

Na Ta, Bo Gao

OAI: oai:igi-global.com:300761 • DOI: 10.4018/JOEUC.300761

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.

End-User Computing Social Sciences and Humanities Human Aspects of Technology