The work aims to propose a PID pulp concentration control method based on multi-objective optimization for the shortcomings of pulp concentration PID control system in time lag, stability and coupling. The pulp production process was analyzed. Combined with the pulp concentration PID control system, multi-attribute decision variables were set, and the corresponding objective functions and constraints were established. The multi-objective optimization was carried out to the pulp concentration PID control process from quality, yield, cost and environment and the pulp concentration PID control model was built based on multi-objective optimization. The model was solved by improved quantum particle swarm algorithm to obtain the Pareto optimal pulp concentration control scheme. The modeling method, optimization algorithm and selection method were coupled to form the whole process of modeling-solving-selection pulp concentration control method. Through the comparison and analysis on decision variables before and after pulp concentration control optimization, the multi-objective optimized PID control method met the controllability requirements of multi-objective optimization with good quality, high yield and low consumption in terms of evaluation indexes. IPSO-PID control method had faster response speed and better robustness compared with the traditional PID control method. In terms of PID parameter optimization, the optimization model set the control parameters in the steady-state stage within 0.05 seconds, with lower steady-state error and better stability. While ensuring the robustness of the system, the PID pulp concentration control system based on multi-objective optimization algorithm can achieve accurate and stable control of pulp concentration, meet the requirements of actual industrial production better, and ensure the quality of paper.