<p>In recent years, dynamic multi-objective optimization problems (DMOPs) have received increasing attention, and numerous strategies have emerged to solve such problems. However, in most existing algorithms, the specific roles of each decision variable are often overlooked. This article proposes an adaptive dynamic multi-objective optimization algorithm utilizing two-stage correlation-based decision variable analysis named ADMOEA-TSCDVA. In the proposed decision variable analysis strategy, the decision variables are initially divided into convergence-related and diversity-related variables based on correlation analysis in the first stage. Subsequently, in the second stage, diversity-related variables are further classified into simple diversity-related and complex diversity-related variables. The results of the decision variable analysis will be used in a dual-model initialization strategy to guide the algorithm in generating a high-quality initial population when changes occur, and in an adaptive strategy to select a suitable static optimization algorithm when the environment remains unchanged. Comprehensive comparative experiments were conducted on two DMOP benchmark test suites against five state-of-the-art algorithms. The results show that the proposed ADMOEA-TSCDVA achieved the best MIGD and MHV results in 63% and 64% of the 57 test cases, demonstrating its excellent performance in dynamic multi-objective optimization scenarios.</p>