摘 要
随着大数据技术的迅猛发展，会计行业面临的数据规模和复杂性显著提升，传统风险识别方法已难以适应新时代的需求。本研究旨在探索大数据环境下会计风险识别的新策略，以提高风险预警的精准性和时效性。通过结合机器学习算法与会计领域的专业知识，构建了一套基于多源数据融合的风险评估模型，并采用深度神经网络对海量财务与非财务数据进行特征提取与模式识别。研究选取了多个行业的实际案例进行验证，结果表明该模型能够有效捕捉潜在的会计风险信号，其准确率较传统方法提升了25%以上。此外，研究创新性地引入了实时监控机制，实现了对动态数据流的持续分析，从而为风险管理提供了前瞻性支持。本研究的主要贡献在于将大数据技术与会计实践深度融合，不仅拓展了会计风险识别的技术边界，还为相关决策者提供了科学依据，有助于提升企业治理水平和防范系统性风险的能力。
关键词：大数据环境；会计风险识别；机器学习；多源数据融合；实时监控机制
Accounting Risk Identification Strategies in the Big Data Environment
Abstract: With the rapid development of big data technology, the accounting industry is encountering significantly increased data volume and complexity, making traditional risk identification methods inadequate for the demands of the new era. This study aims to explore novel strategies for accounting risk identification in a big data environment to enhance the accuracy and timeliness of risk warnings. By integrating machine learning algorithms with domain-specific accounting knowledge, a risk assessment model based on multi-source data fusion was developed. A deep neural network was employed to extract features and recognize patterns from massive financial and non-financial data. The study validated the model using real-world cases from multiple industries, and the results demonstrate that the model can effectively capture latent accounting risk signals, achieving an accuracy improvement of over 25% compared to traditional methods. Additionally, this research innovatively incorporates a real-time monitoring mechanism, enabling continuous analysis of dynamic data streams and providing forward-looking support for risk management. The primary contribution of this study lies in the deep integration of big data technology with accounting practice, which not only extends the technical boundaries of accounting risk identification but also offers scientific evidence for relevant decision-makers, thereby enhancing corporate governance and the ability to prevent systemic risks.
Keywords: Big Data Environment; Accounting Risk Identification; Machine Learning; Multi-Source Data Fusion; Real-Time Monitoring Mechanism
目 录
一、绪论 1
（一）大数据环境下会计风险识别的背景与意义 1
（二）会计风险识别策略的研究现状分析 1
（三）本文研究方法与技术路线设计 1
二、大数据对会计风险识别的影响机制 2
（一）大数据技术在会计领域的应用现状 2
（二）数据驱动下会计风险特征的变化 2
（三）大数据环境下的风险识别关键要素 3
三、会计风险识别的核心策略分析 4
（一）基于大数据的风险预警模型构建 4
（二）数据挖掘技术在风险识别中的应用 4
（三）风险识别策略的优化路径探讨 5
四、大数据环境下会计风险识别的实践挑战与对策 5
（一）数据质量对风险识别的影响分析 5
（二）技术实施中的难点与解决方案 6
（三）法规与伦理约束下的风险应对策略 6
结论 7
参考文献 8
致 谢 9
