Construction and Optimal Control Method of Enterprise Information Flaw Risk Contagion Model Based on the Improved LDA Model

In this study, we construct a risk contagion model for corporate information disclosure using complex network methods and incorporate the manipulative perspective of management tone into it. We employ an enhanced LDA model to analyze and refine the relevant data and models presented in this paper. The results of quantitative analysis show that the improved LDA algorithm optimizes the classification decision boundary, making similar samples closer and different samples more dispersed, thus improving the classification accuracy. Additionally, we combine multi-objective evolutionary optimization techniques with an improved particle swarm optimization algorithm to solve the proposed model while incorporating enhancements through the use of weighted Smote algorithm. The quantization results show that using the weighted Smote algorithm to deal with the imbalance in the dataset significantly improves the classification performance. Furthermore, we compare our proposed method with classical algorithms on four real enterprise information disclosure datasets and observe that our approach exhibits higher efficiency and accuracy compared to traditional optimal control methods. Accounting information disclosure reveals moral hazard and adverse selection, alleviating information asymmetry. Transparent information improves the availability of financing, preventing liquidity risk. High-quality information disclosure reduces financing costs, alleviates confidence crises, ensures capital adequacy, and avoids capital outflows. Research constructs a corporate information disclosure risk contagion model, using an improved LDA model and multi-objective evolutionary optimization methods for analysis, showing high efficiency and good accuracy, effectively controlling environmental and related effects.

@article{Wang2024,
  title     = {Construction and Optimal Control Method of Enterprise Information Flaw Risk Contagion Model Based on the Improved LDA Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jun Wang and Zhanhong Zhou},
  doi       = {10.14569/IJACSA.2024.0151234},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151234}
}