Profit Estimation Model and Financial Risk Prediction Combining Multi-scale Convolutional Feature Extractor and BGRU Model

Abstract

In response to the inaccuracy of financial risk prediction and profit prediction for enterprises, a financial risk prediction model based on the concept of graph networks was studied and designed. This experiment combined multi-scale feature extraction and sequence analysis methods. In addition, the model adopted a structurally concise and effective bidirectional gated recurrent unit to capture temporal relationships in time series data. In the design of profit prediction models, it combined multi-scale advantages and attention mechanisms. The latter enhanced the recognition and utilization of influential features, which could improve predictive ability and practical value. These results confirmed that after iterative training, the accuracy of this model had significantly improved to 98.03%. The F1 score of the financial risk prediction model reached 0.98, demonstrating excellent performance. The profit prediction model performed better than other models in both regression and classification problem indicators, with an error close to 0 and a mean square error of 0.0232, indicating that the model had extremely high prediction accuracy. Therefore, both models have strong predictive ability and have practical application significance.