Journal of Engineering, Project, and Production Management, 2026, 16(3), 2026-46
Precise Enterprise Budget Control by Integrating BP Neural Networks and Decision Tree Algorithms
Associate Professor, School of Economics and Management, Anyang University, Anyang, 455000, China, E-mail:chenli-lichen@outlook.com
Project Management
Received January 13, 2026; revised April 12, 2026; May 6, 2026; accepted May 7, 2026
Available online May 29, 2026
Abstract: Precise budget regulation ensures resilience against future crises. However, large-scale and disorganized enterprise datasets often reduce prediction accuracy and weaken budget control. To address this, a decision tree algorithm is integrated with an improved Back Propagation (BP) algorithm enhanced by an additional momentum term, enabling more effective processing of enterprise data. The proposed fusion approach achieved prediction errors below 2% on the Iris dataset. Applied to enterprise budgeting, it achieved over 95% accuracy in forecasting net profit, monthly savings, and risk tolerance. These results demonstrate that the model can accurately predict departmental budgets, enabling precise budget control. Consequently, management can allocate funds more rationally, reduce resource waste, and enhance overall operational efficiency. This study provides a robust method for improving budget prediction accuracy and control, offering significant benefits for enterprise financial planning and long-term stability.
Keywords: Decision tree algorithm, back propagation algorithm, enterprise budget, prediction, precision control.
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Citation: Chen, L. (2026). Precise
Enterprise Budget Control by Integrating BP
Neural Networks and Decision Tree Algorithms. Journal of Engineering, Project, and Production Management,
16(3), 2026-46.
