The current research on Multi-Objective Evolutionary Algorithm （MOEA） in dealing with Constrained Multi-objective Optimization Problems （CMOPs） is mainly to solve the single type of constraints， and in dealing with different kinds of complex constraints， the algorithm is difficult to converge or has poor population distribution. To solve this problem， based on the framework of MOEA based on Decomposition （MOEA/D）， an Adaptive Reference Vector based Constrained Multi-Objective Evolutionary Algorithm （ARVCMOEA） was proposed. Firstly， the reference vectors were divided into two parts： the main reference vectors and the auxiliary reference vectors. Then， in the initial phase of the algorithm， the unconstrained auxiliary reference vectors were used to guide the population to quickly cross the infeasible interval. Finally， the distribution and search ability of the algorithm were improved by adaptively adjusting positions of the auxiliary reference vectors and weakening the distribution requirements. Experiments were carried out on 30 test functions with different kinds of complex constraints. The results show that the proposed algorithm can converge well with different kinds of constraints， and it is superior to Non-dominated Sorting Genetic Algorithm II （NSGA-II）， Constraint-MOEA/D （C-MOEA/D） and MOEA/D with Detect-And-Escape strategy （MOEA/D-DAE） in overall performance， and it can obtain better results on some test functions than the current excellent Coevolutionary Constrained Multi-objective Optimization framework （CCMO）， verifying that the proposed algorithm has excellent performance in the face of different kinds of CMOPs.