Financial crisis early warning system of manufacturing enterprises based on BBO algorithm to optimize ResNet network

In the context of global economic fluctuations and manufacturing transformation and upgrading, the importance of early warning systems for the corporate financial crisis has become increasingly prominent. This paper proposes a ResNet deep learning network optimized based on a BBO (Biogeography-Based Optimization) algorithm for early warning of financial crises in manufacturing enterprises. Through in-depth analysis of the financial data of 100 manufacturing enterprises in the past 5 years, the optimization mechanism of the BBO algorithm is designed first and applied to the structure and parameter optimization of the ResNet network. Subsequently, the feature extraction method based on deep learning automatically learns from financial data to deeper feature representation. The experimental results show that the accuracy rate of the optimized ResNet network in financial crisis early warning reaches 87%, which is 25% higher than that of the traditional method, and the false alarm rate is reduced to 5%, which significantly improves the performance and reliability of the early warning system. The experimental results show that the optimized model can accurately predict the financial crisis and provide detailed early warning reports, effectively evaluating the potential risk points and levels.