The establishment of a safe and effective recurrent neural network enterprise risk early warning model is conducive to the effective risk assessment of bank loans, provides a reliable guarantee for the risk management of various enterprises, and meets the actual needs of current investors for risk control. In order to solve the shortcomings of the existing research on the enterprise risk early warning model of RNN, this paper discusses the functional equation of RNN and the concept of non-functional requirements of enterprise risk early warning. The parameter settings and sample data of the risk warning model application are briefly introduced. And design and discuss the structure of enterprise risk early warning model about cyclic neural network. Finally, the accuracy rate of the enterprise risk early warning model designed in this paper about enterprise health, mild enterprise risk and severe enterprise risk is analyzed. , recall rate and true negative rate for experimental data analysis, in which the model's early warning accuracy rate for enterprise health is 0.96% to 0.98%, the early warning recall rate for mild enterprise risks is as high as 0.98%, and the early warning rate for severe enterprise risks is as high as 0.98%. The true negative rate is as high as 0.93%, thus verifying the reliability of the enterprise risk early warning model based on the recurrent neural network.

Xiaokui Zhao. Enterprise Risk Early Warning Model Based on Recurrent Neural Network. International Journal of Neural Network (2022), Vol. 3, Issue 4: 17-25. https://doi.org/10.38007/NN.2022.030403.

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