Dynamic multi-objective optimization problems require evolutionary algorithms(EAs) to track the changing Pareto-optimal front(PF) at different times, then can respond effectively and timely when environmental changes are detected. In order to solve the above problem, a dynamic multi-objective optimization algorithm based on the relationship of decision variables is proposed. Firstly, through the detection mechanism of the contribution of decision variables to convergence and diversity, the decision variables are divided into convergence decision variables(CV) and diversity decision variables(DV). Secondly, different optimization strategies are adopted for different types of decision variables. And a local search diversity maintenance mechanism is proposed to make individuals more evenly distributed in the Pareto front. Finally, the non-dominated sort of the combined solutions generated by the two parts constitutes the population in the new environment. In order to verify the performance of relationship of decision variables, relationship of decision variables is compared with three dynamic multi-objective optimization evolutionary algorithms on the 15 benchmark functions. Experimental results demonstrate that the DVR algorithm exhibits better convergence and distribution than the other three algorithms.