Research on optimization and application of enterprise performance prediction model based on deep learning algorithm

Traditional methods for predicting corporate performance rely on historical financial data and linear assumptions, making it difficult to capture complex nonlinear factors and potential information in high-dimensional data. To tackle this problem, the research introduces a deep learning approach utilizing Long Short-Term Memory (LSTM) networks aimed at enhancing the accuracy of predicting enterprise performance. The model processes the time series data by normalization and sliding window technology, and optimizes it by grid search, Dropout, early stop method and L2 regularization, which effectively improves the generalization ability and robustness of the model. The experimental data comes from the financial, market and macro indicators of listed companies in Shanghai and Shenzhen A-shares from 2010 to 2023, covering many industries. The findings indicate that the enhanced LSTM model achieves an accuracy of 86.7% on the test set and attains an F1 score of 80.3% for the high-growth category, outperforming both the conventional model and the GRU significantly. For the regression task, it records the lowest mean squared error (MSE) on the test set, which is 0.29. In addition, the model still shows good generalization ability and robustness under cross-industry testing and noise interference. In practical application, the model provides accurate dynamic decision support and risk early warning for enterprises, and verifies the effectiveness of deep learning in complex time series forecasting tasks.