摘 要
随着经济环境的复杂化和市场竞争的加剧，企业面临的财务风险日益多样化和隐蔽化，传统的财务分析方法已难以满足现代企业管理的需求。为此，本研究旨在构建一种基于多维数据的企业财务风险预警模型，以实现对企业潜在财务风险的早期识别与精准评估。研究综合运用了统计分析、机器学习及大数据技术，选取了涵盖财务指标、市场表现及行业特征等多个维度的数据集，并通过对比多种算法性能，最终确立了基于集成学习的优化模型。实证结果表明，该模型在预测准确率、灵敏度及稳定性等方面均显著优于传统方法，能够有效捕捉企业财务状况的动态变化并提供决策支持。此外，本研究创新性地引入了非结构化数据（如舆情信息）作为补充变量，进一步提升了模型的解释力和适用性。总体而言，本研究不仅为企业的风险管理提供了科学工具，也为相关领域的理论发展做出了重要贡献，具有较高的实践价值和推广潜力。
关键词：财务风险预警；集成学习；多维数据；非结构化数据；预测准确率
Abstract
With the increasing complexity of economic environments and intensifying market competition, the financial risks faced by enterprises are becoming more diversified and concealed. Traditional financial analysis methods are no longer sufficient to meet the demands of modern enterprise management. To address this challenge, this study aims to construct a multi-dimensional data-based early warning model for enterprise financial risks, enabling the early identification and precise evaluation of potential financial risks. By integrating statistical analysis, machine learning, and big data technologies, a comprehensive dataset was selected, encompassing multiple dimensions such as financial indicators, market performance, and industry characteristics. Through comparative analysis of various algorithm performances, an optimized model based on ensemble learning was ultimately established. Empirical results demonstrate that this model significantly outperforms traditional methods in terms of prediction accuracy, sensitivity, and stability, effectively capturing the dynamic changes in enterprise financial conditions and providing decision-making support. Additionally, this study innovatively incorporates unstructured data, such as public sentiment information, as supplementary variables, further enhancing the model's explanatory power and applicability. Overall, this research not only provides enterprises with a scientific tool for risk management but also makes significant contributions to the theoretical development of related fields, showcasing substantial practical value and potential for widespread application.
Keywords: Financial Risk Warning；Integrated Learning；Multi-dimensional Data；Unstructured Data；Prediction Accuracy Rate
目 录
摘 要 I
Abstract II
一、绪论 1
（一）企业财务风险预警研究背景与意义 1
（二）国内外财务风险预警模型研究现状 1
（三）研究方法与技术路线 1
二、财务风险预警模型的理论基础 2
（一）财务风险的基本概念与分类 2
（二）预警模型的核心理论框架 2
（三）数据驱动的预警模型构建原理 3
（四）理论在实际应用中的挑战 3
三、财务风险预警模型的设计与实现 4
（一）模型设计的关键要素分析 4
（二）数据选择与预处理方法研究 4
（三）预警指标体系的构建与优化 5
（四）模型算法的选择与实现路径 5
四、财务风险预警模型的应用案例分析 6
（一）实证研究的企业样本选择 6
（二）模型在企业财务风险识别中的应用 6
（三）预警结果的评估与改进策略 7
（四）案例对企业风险管理的启示 8
结 论 8
致 谢 10
参考文献 11
