For a class of two block nonconvex optimization problems, a majorized alternating direction method of multipliers with Bregman distance is proposed. In order to make the subproblem of the problem easier to solve, maximizing the smooth term in the objective function with linear processing and a Bergman distance is added to the x-subproblem and the y-subproblem at the same time. Under appropriate assumptions, the global convergence of the algorithm is established. Secondly, when the benefit function satisfies the KL property, the strong convergence of the algorithm is established. Numerical experiments are carried out on the algorithm, and the results show that the algorithm is an effective method.