When using evolutionary algorithms to solve expensive many-objective optimization problems, the many-objective leads to difficulties in balancing convergence and diversity and makes convergence difficult when computational resources are limited due to high consumption costs. Therefore, this paper proposes a decomposition and cluster based expensive many-objective evolutionary algorithm(DC-EMEA), which uses the Kriging model to approximate the objective function and reduces the number of evaluations of real expensive functions. When the optimizer searches for the optimal solution set of the model, the objective space is decomposed with the help of the reference vector, which is conducive to the balance of convergence and diversity. At the same time, two rounds of selection are adopted to ensure that the offspring population size is the same as that of the parents, providing more options for the selection of individuals for real evaluation by the infill criterion and improving the search efficiency. Meanwhile, an adaptive infill criterion is proposed to firstly divide the population into k subpopulations using the K-means algorithm. Then, by dividing the neighborhood, the subpopulations are adaptively divided into different types, and individuals are selected according to the types of subpopulations to improve the utilization of computational resources. In the selection of individuals, the focus is on the maintenance of convergence pressure to improve the convergence speed. Finally, the selected individuals are used to update the model and the archive. The experiments show that the DC-EMEA can balance convergence and diversity well and has a strong convergence ability.