In real life, there are many dynamic multi-objective optimization problems (DMOPs), which require algorithms to quickly converge to the new Pareto front and maintain the diversity of the solution set. As the complexity of the Pareto front increases, this problem is more prominent. For this problem, a dynamic multi-objective optimization algorithm based on multi-regional center point prediction (MCPDMO) is proposed. Firstly, the population is divided into multiple sub-regions according to the degree of environmental change, which makes the allocation of individuals more adaptable to the dynamic environment. Secondly, the center point of each sub-region is calculated respectively. The time series are established with the center point of different sub-regions at different times, and the difference model is used to predict the optimal solution set of the new environment. It improves the algorithm’s ability to respond to changes in different environments. To verify the effectiveness of the algorithm, the simulation experiments are conducted on 10 test functions with 3 dynamic multi-objective optimization algorithms. The results show that the algorithm exhibits better convergence and distribution in dynamic problems with complex Pareto front.