This study proposed a diversity classification and distance regression assisted evolutionary algorithm (DCDREA) to solve expensive many-objective optimization problems (MaOPs). In DCDREA, the random forest (RF) is adopted as the global classification surrogate model. All the solutions in the population are as the training samples and classified into positive or negative samples according to whether they are minimum correlative solutions, so that the model can learn the classification criteria contained in the training samples. The global classification surrogate model is mainly used to filter the newly generated candidates to obtain a group of promising candidates. In addition, Kriging is adopted as the local regression surrogate model, where the solutions closest to the current candidates in the population are selected as the training samples, and the distance between the training samples and the ideal point is approximated by the model. Then, by the K-means method, the candidates are divided into μ clusters, and from each cluster, one candidate is selected for real function evaluation. In the experimental part, the DTLZ suite with large scale 3, 4, 6, 8, and 10 objectives was used to compare DCDREA with the current popular surrogate-assisted evolutionary algorithms. For different test problems, each algorithm was run independently for 20 times. Then the inverted generational distance (IGD) and algorithm running time were counted. At last, the Wilcoxon rank sum test was used to analyze the results. The result comparison shows that DCDREA performs better in most cases, indicating that DCDREA has sound effectiveness and feasibility.