With the rapid development of the digital economy, efficient and intelligent system optimization strategies have become the key to improving economic operational efficiency. This study adopted the Fuzzy PID (Proportional-Integral-Derivative) intelligent control algorithm to dynamically adjust and optimize key performance indicators in the digital economy. A fuzzy logic-based adaptive tuning mechanism was designed, which can adjust PID control parameters in real-time to adapt to various complex situations in economic activities. The effectiveness of the fuzzy PID algorithm in system resource allocation, efficiency improvement, and cost control was verified through comparative experiments. During the continuous supply of raw materials (time stamp 0–2 h), inventory gradually increased; production remained stable; order fulfillment rate remained at 100%. The results of this study not only provide more accurate and flexible tools for decision-making in digital economy systems, but also open up new possibilities for the application of intelligent control technology in the economic field, with important theoretical value and broad application prospects.