With the continuous evolution of financial risks faced by enterprises, the traditional financial crisis prediction methods gradually show their limitations. Therefore, this paper proposes an innovative financial crisis prediction model combining competitive particle swarm optimization (CSO) algorithm with financial and non-financial data fusion. In the data fusion stage, the model deeply discusses the interaction between different data types, uses the CSO algorithm to dynamically optimize the model parameters, and realizes efficient feature selection to extract the most representative feature subset for financial crisis prediction. Based on the selected features, the prediction model constructed significantly improves the prediction accuracy and stability. Through systematic comparison with the traditional forecasting model, the experimental results show that the proposed model has significant advantages in accuracy and robustness, demonstrating good practicability, and providing scientific basis and reference for enterprises' financial decision-making.

Peng Dong. Research and Analysis of Financial Crisis Prediction Model Based on the Fusion of Financial and Non-Financial Data with CSO Algorithm. Academic Journal of Business & Management (2024) Vol. 6, Issue 11: 56-60. https://doi.org/10.25236/AJBM.2024.061109.

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