This investigation outlines a new intelligent system to assist in decision-making for enterprise organisational changes in the context of the digital economy. The innovations of this study are threefold: First, the creation of a multi-dimensional decision model defined by the real-time indicators from the digital economy, as well as traditional metrics of organisational change for structural evolution. Second, the application of a hybrid intelligent algorithm that incorporates deep learning with knowledge graphs enables the processing of both structured and unstructured data at the enterprise level, thereby offering broader decision-making support than standard systems. Third, the development of a system that provides optimised decision recommendations based on what happens after the decision is implemented, thus closing the gap between system design and reality. Results from practical tests conducted in several enterprises substantiate that the proposed system has 35% greater efficiency in making decisions and 42% lower risks in implementing organisational changes than the traditional methods. This development has a considerable impact on the teaching and practice of intelligent decision support in enterprise digital transformation, posing a new approach to managing organisational changes in the digital economy.
Kexin Zhang (Sat,) studied this question.