Objective Aiming at the problems of large inertia large hysteresis and large overshooting in the control of cold air equalization pressure of hot blast furnace a sliding mode control strategy based on dual-objective regionalized particle swarm algorithm for cold air pressure equalization of hot blast furnace was proposed. Methods In the proposed strategy the cold air flow rate was used as the control quantity the cold air pressure was set as the controlled quantity the sliding mode control algorithm was combined with the particle swarm optimization algorithm and the conventional sliding mode algorithm was selected and improved. The exponential convergence law was used to reduce the influence of external disturbances on the system and a two-objective regionalized particle swarm algorithm was added to optimize the sliding mode switching gain coefficients and exponential convergence coefficients. By taking the average ranking of particles environmental test parameters and other indexes as the optimization conditions the optimal sliding mode control law was taken to regulate the cold air pressure so as to realize the optimal control effect of equalizing pressure. Results The simulation results showed that the designed control strategy stabilized at 58. 2s with 4. 1% overshoot under the fit condition and stabilized at 72. 4s with only 17. 6% overshoot under the mismatch and disturbance conditions. Compared with serial PID control and conventional sliding mode control the proposed strategy has good overshooting and fast stabilization. Conclusion The engineering application shows that the deviation of cold air pressure fluctuation is only ±6. 48 Kpa. The proposed control strategy has the characteristics of high stability fast response and small overshoot which can be well applied in the actual production and meet the requirements of cold air pressure equalization of hot blast furnaces.