Entity relation extraction is one of the most important researches in the field of information extraction. Previous researches focus on extracting various kinds of lexical or semantic features from the context where the related entities appeared, and one kind of classifiers (such as SVM) is used to extract the entity relation, but this kind of methods ignore the impact of the classifier performance on the entity relation extraction. Since SVM classifier has low accuracy for the test samples near the hyperplane, a method based on double-vote mechanism is designed for determining the fuzzy SVM samples. In the method, SVM classifier is used to classify the non-fuzzy samples directly; then, k-nearest neighbors (KNN) algorithm is applied to classify the fuzzy ones. The experiment on the data provided by SemEval-2010 evaluation task shows that the method can improve the performance of the entity relation extraction.