Research on Enterprise Financial Risk Early Warning Based on Deep Learning

Aiming at the high-dimensional and nonlinear characteristics of time series data in enterprise financial risk early warning, this paper proposes a hybrid deep learning model based on LSTM and GRU. Using the financial data of 800 listed companies in the new energy industry and taking the ST risk label as the benchmark, the model training is optimized with cross-entropy loss and category weight strategy. The results show that the hybrid model significantly outperforms single LSTM and GRU models in precision, ST recall, and mAP value, verifying the enhancement effect of model complementarity on early warning performance. This study provides a new idea for the multi-model collaborative application of deep learning in financial risk management, and its high recall rate has practical value for reducing the risk of enterprise crisis underreporting.