Abstract: In order to solve the bottlenecks faced by the high-quality development of the digital economy, such as insufficient factor allocation efficiency and single innovation model, and explore the empowerment mechanism and path optimization strategy of generative AI, this paper systematically constructs a theoretical framework for the deep integration of generative AI and the digital economy. Firstly, this paper proposes a four-level structure of "basic support-technology engine-industrial empowerment-governance guarantee" to clarify the synergistic relationship between elements at all levels. Secondly, this paper reveals that generative AI drives the development of the digital economy through the intelligent closed-loop operation mechanism of "perception-cognition-decision-feedback", and explains the dual paths of internal optimization of digital industrialization and external collaboration of industrial digitalization. Taking the manufacturing industry in Shaanxi Province as an example, combined with the economic data from 2016 to 2024, this paper systematically analyzes the practical difficulties faced by generative AI in industrial applications such as energy, chemical, and equipment manufacturing, such as complex knowledge representation, data sharing barriers, and insufficient technical resources. The study finds that the main current challenges include obstacles to cross-domain data sharing, insufficient model robustness and security, and lack of human-machine collaborative planning. Accordingly, countermeasures such as building a data element circulation system, technical governance mechanism, and integrated development system are proposed. The theoretical contribution of this paper lies in the construction of a complete theoretical system covering architecture, mechanism and path, and reveals the deep contradictions of technology implementation through case analysis, providing theoretical guidance and practical reference for the high-quality development of the digital economy.