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With the development of modern society, big data emerge as the product of high-tech. According to big data, enterprises can know about their financial situations in detail through the statistical analysis, which is the basis for establishing appropriate financial management modes and formulating financial development strategies, achieving the reuse of financial management modes.
For the sustainable reuse of financial management mode, scholars in China and abroad have conducted a lot of researches. Xu et al. (2020) explored the relations between innovation investment and financial sustainability in the energy industry and the roles of executive incentives. The results show that innovation investment has a heterogeneous impact on the business performance of different energy enterprises in different periods. Cheah et al. (2019) established a framework to evaluate the impact of the most prominent internal resources (i.e. entrepreneurial orientation, social significance, and business planning) regulated by the financial and social performance of social enterprises. Qi et al. (2020) used Back Propagation Neural Networks (BPNN) to evaluate the credit risk and personal information risk of network finance. The results show that credit risks and personal information risks are the most important factors affecting the future development of network finance. In turn, they may hinder the development of Internet finance in some cases. Zhang et al. (2021) used long-short term memory (LSTM) to forecast the change in stock price. Compared with artificial neural networks (ANN), LSTM is more suitable for dealing with nonlinear, non-stationary, and complex financial time series. Investor attention agents are used as market variables to improve the forecast accuracy, such as price, trading volume, and other technical indexes. The empirical results show that the LSTM model using the online investor attention agent is better than other models, and has the highest forecast accuracy and reasonable time consumption. Zhang et al. (2019) developed a personal financial scoring model based on a hybrid support vector machine (SVM) using three technologies to evaluate the candidates' short-distance return score from the candidates' information highlights .
The sustainable development of corporate financial management mode is a hot topic. If enterprises want to maintain sustainable financial development, they should employ an efficient financial management mode and long-term smooth operation. In this case, a financial crisis forecast system is established based on deep learning (DL). Through the financial data collected by LSTM, the financial situation of enterprises is analyzed, which provides a reference for enterprise management and promotes the sustainable development of corporate financial management mode.