Enterprise accounting information recognition system based on data mining

Abstract

This article focuses on the research of enterprise accounting information recognition system based on data mining, pointing out that traditional accounting information systems and management accounting methods in the era of big data rely too much on internal historical financial data, making it difficult to effectively process key internal and external unstructured data, resulting in insufficient depth of strategic decision analysis. Data mining technology provides a new path for integrating internal and external data and building a strategic management accounting system. A five layer strategic management accounting system framework was developed, which includes a basic theoretical layer and a data storage layer. The framework integrates theories such as Porter’s Five Forces model and regression analysis techniques. Taking e-commerce company M as a case study, the data mining application in scenarios such as intelligent supply chain replenishment was analyzed. Logistic regression model was used to identify the authenticity of financial reports, and LSTM neural network was used to predict sales data. The results showed that after applying the framework, M company’s market share reached 46% and growth rate was 36% in the 12th month. The logistics unit cost supported order volume increased by 400%. This study achieved interdisciplinary integration of data mining and strategic management accounting, providing enterprises with information recognition and strategic decision-making tools to help them optimize management and maintain core competitiveness through data-driven optimization in complex environments.