Research on Enterprise Management Data Analysis Based on Artificial Intelligence

Abstract

With the continuous improvement of the digitalization level of enterprises, business data presents the characteristics of multi-source, high-dimensional and strong time series. Traditional analysis methods are difficult to meet the needs of dynamic prediction and risk identification. This paper designs and implements an enterprise business data analysis system based on artificial intelligence, integrates the LSTM-Attention model for trend prediction, combines XGBoost to achieve multi-dimensional risk classification, and improves the interpretability of the results through SHAP decomposition and causal graphs. The experiment is based on the financial data of A-share listed companies in 2023. The results show that the method proposed in this paper is superior to the comparative scheme in terms of prediction accuracy, risk identification ability and improvement of operating return rate, and has good robustness. The system can be widely used in scenarios such as corporate financial management, strategy formulation and dynamic business early warning.