In order to solve the problem of large time and low accuracy in the process of multi-classifier fusion,a Parallel Multi-Classifier Fusion Approach based on Selective Ensemble (PMCF-SE) is proposed by adopting both the improved Baggingmethod and MapReduce technique. Our approach is based on the MapReduce parallel computing framework.In the Map phase,the base classifiers with better classification performance are selected. In the Reduce phase,the base classifiers of greater diversity are selected, and then the selected base classifiers are fused with the D-S evidence theory. Experimental results show that, compared with the stand-alone environment, the execution efficiency of the classification model in the cluster environment is improved. PMCF-SE has higher classification accuracy than the Bagging algorithm under different numbers of base classifiers.