The distribution and convergence of solutions are poor when constrained many-objective optimization problems are solved with multi-objective evolutionary algorithm, which tend to fall into local optimal solutions. We propose a decomposition-based constrained dominance principle NSGA-II(DBCDP-NSGA-II) based on the fusion of Pareto dominance, decomposition and constraint dominance. In the study, based on retaining the fast non-dominant ranking in NSGA-II, Pareto dominance is employed firstly to dominate population. Then according to the nature of the solution, the DBCDP is adopted to punish the equivalent solutions. The feasible and infeasible solutions in sparse regions are preserved to improve the distribution, diversity and convergence of the population. Finally, the critical values are reordered by the vertical distance and the crowding distance from the individual to the weight vector until N optimal individuals are selected for the next iteration. Using constrained DTLZ as an example, the algorithm is compared with C-NSGA-II, C-MOEA/D, C-MOEA/DD and C-NSGA-III. The results show that it has more uniform distribution and better global convergence performance than the other four algorithms.