<p>In recent years, Dynamic Multi-Objective Optimization Problems (DMOPs) have attracted considerable attention in the field of evolutionary computation. Since most real-world optimization problems are influenced by dynamic environmental changes, how to effectively adapt to these changes and identify more adaptive solution sets has become increasingly important. However, most existing solution methods can only handle a single type of dynamic change, making it difficult to adapt to diverse environmental variations. To address these problems, this paper proposes a dynamic multi-objective evolutionary algorithm based on environmental change intensity assessment. The core idea of the proposed algorithm is to dynamically monitor the intensity of environmental changes and adopt different population initialization strategies to handle various types of environmental variations. Specifically, it introduces an environmental intensity assessment mechanism that evaluates the extent of environmental changes based on the frequency and degrees of variations. In the case of minor environmental changes, a local re-initialization strategy is employed, preserving elite individuals from the previous population and applying slight perturbations to adjust them, enabling the population to quickly adapt to new changes. For more complex environmental changes, the algorithm introduces an adaptive step-size mechanism. This mechanism accelerates convergence to the true Pareto front by adjusting the position of the population. This paper conducts experiments on DF benchmark problems exhibiting three dynamic characteristics to evaluate the performance of the proposed algorithm. The results demonstrate that the method effectively identifies environmental changes and achieves dynamic tracking and adaptation in complex environments.</p>