Path optimization of enterprise economic management system in digital transformation

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.