Dynamic Selection of integration algorithm is usually accompanied with the situation that there is no way to avoid the misclassification when the local classifier can not classify the test pattern correctly, accordingly a novel dynamic SVM selection ensemble algorithm based on diversity-clustering is proposed. Clustering is applied to training samples firstly in this method. To every clustering, appropriate classifier ensemble is selected based on accuracy and diversity, and the sample areas which are misclassified by the classifier ensemble for every clustering is demarcated, and a set of classifier ensemble for it is designed. During testing, the test sample is classified by the appropriate classifier ensemble based on the clustering to which it belongs and the distance between it and the misclassified sample areas. Using this method can remarkably reduce the blind regions while the test sample is very close to the misclassified areas mentioned above. Experimental results show the effectiveness of this method. Compared with Bagging-SVM and literature \[10\] on UCI data set, the testing speed can be guaranteed and simultaneously the classification accuracy can be effectively improved by using this algorithm.