Abstract: The energy consumption of industrial fan systems accounts for a significant proportion of the total energy consumption in large buildings and industrial facilities. Traditional control methods suffer from issues such as response lag and poor adaptability. An intelligent control strategy that integrates fuzzy PID control with particle swarm optimization algorithm is proposed, and a real-time monitoring and dynamic adjustment system for fan operating status is constructed. By establishing a fan power consumption prediction model, adaptive parameter optimization under load changes is achieved. Simulation verification indicates that this method improves energy-saving effects by 23.7% compared to traditional frequency conversion control, shortens response time by 42%, and significantly enhances system stability. Engineering application cases demonstrate that after the renovation of the HVAC system in a research center in East China, the annual cooling energy consumption is reduced by 25%, and the fluctuation range of chilled water temperature is improved from ±2 °C to ±0.3 °C. The algorithm exhibits good robustness and practicality under various working conditions, providing effective technical support for practical engineering applications.