Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

May 22, 2024


Pages


DOI

Article

Path optimization of enterprise economic management system in digital transformation

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Authors

Guang Yang Affiliation:
Xinxiang Vocational and Technical College, Xinxiang, Henan, 453000, China.


Abstract

In the digital economy era, digital transformation aligns with sustainable development and national policies. This research examines enterprise economic output through human and physical capital driven by digital transformation. Utilizing the TrAdaBoost algorithm in transfer learning, we predict economic risks for enterprises. We propose an optimization path for digital economic management systems. Furthermore, we empirically analyze the current financial management and challenges of digital transformation at Company X, laying the groundwork for identifying and analyzing digital transformation risks. Our findings indicate the significant influence of risk-free interest rate (0.1781), GDP (0.1732), and money supply (0.1668) on enterprise economic risk, guiding enterprises to mitigate these impacts during digital transformation.


Keywords

Digital transformation, Transfer learning, TrAdaBoost algorithm, Enterprise economic management, 68T05


Citation

Yang, G. (2024). Path optimization of enterprise economic management system in digital transformation. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1134

Published by: Engineering Journals

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