Abstract: When Support Vector Machine（SVM） is used to solve the classification problems，the samples nearby the SVM hyperplanes are more easily misclassified.To solve this problem，the Reverse K-Nearest Neighbor method is introduced into the classification problems，and the Reverse K-Nearest Neighbor classification method（RKNN） is presented.And then，a new classification algorithm based on Support Vector Machine and Reverse K-Nearest Neighbor classification method（SVM-RKNN） is presented.At last，in order to avoid the one-sidedness problems which may be produced by one single classifier，the multi-fusion method based on SVM-RKNN is presented.The experimental results show that the average forecast accuracy of the SVM-RKNN method increases 2.13% than the SVM method，and the average forecast accuracy of the multi-fusion method based on SVM-RKNN increases 2.54% and 0.41% than the SVM and SVM-RKNN method respectively.