# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 1 Introduction  
Against the backdrop of continued global economic uncertainty and the accelerated digital transformation of Chinese industries, enterprise financial risk management is facing severe challenges. According to a report by the United Nations, the global economic growth rate is expected to drop from 2.9% in 2024 to 2.4% in 2025. The global economy is recovering sluggishly, and domestic development is also under tremendous pressure. Some listed companies are facing greater operational challenges, with 29 entering bankruptcy and restructuring proceedings in 2024. This represents a 16% increase on the 25 companies in 2023, highlighting the vulnerability of enterprises' risk resistance capacity. In this situation, the requirement for financial distress prediction to be both timely and accurate has significantly increased. However, traditional methods have a limited ability to identify nonlinear relationships and time dependence, making it difficult to respond effectively to the current complex risk characteristics.Therefore, this paper proposes a fusion model combining a convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network, and an attention mechanism (AT) with a Hyperband algorithm for hyperparameter optimization. The aim is to construct a dynamic warning system that is both highly precise and robust.  
This article proposes a deep learning model that incorporates a CNN, a BiLSTM, and an attention mechanism. This model achieves a prediction accuracy of 0.994 on the test set, outperforming existing mainstream models. The model effectively combines CNN's localfeature extraction capability, BiLSTM's time series modeling advantage and the attention mechanism's dynamic weighting of key features. It also introduces the SMOTE algorithm to mitigate the bias caused by sample category imbalance, improving the recognition of distressed enterprises. The Hyperband algorithm automatically optimizes the model's hyperparameter configuration, enhancing its overall performance and training efficiency. The model has high practical value and application potential in the early warning of financial risk and decision-making.  
This article enriches the theoretical framework for predicting financial risk, deepens our understanding of the dynamic allocation of temporal features and weights of key variables in deep neural network models, and encourages the exploration of applying the attention mechanism to analyze financial data. The introduction of an automated hyperparameter optimization method improves the efficiency and generalization ability of model training and reduces the subjectivity of manual parameter tuning. Meanwhile, validation based on repeated case company trials and the latest 2025 data enhances the model's robustness and applicability, providing more solid empirical support for financial risk management theory and laying the methodological groundwork for subsequent related research.  
This article is structured as follows: Section 1 is the introduction section, outlining the research background, significance, and main contributions; Section 2 is the literature review section, reviewing the research progress in financial distress prediction and deep learning modeling at home and abroad; Section 3 is the methodology section, describing the structural design and hyperparameter optimization strategy of the proposed model; Section 4 is the experimental section, which describes the data sources, preprocessing procedure and experimental protocol; Section 5 is the results section, which covers the performance evaluation of the model structure, comparative analysis with mainstream models, and stability validation; and Section 6 is the conclusion section, which summarizes the research results, discusses the limitations, and proposes the future research direction.