Abstract: Support vector machine(SVM), one of machine learning methods, is very impressive for its good generalization and powerful nonlinearly processing ability. SVM was combined with national matriculation, where scores of six mock exams are taken as training data to predict the final admission scores. Three situations were considered. First, the scores of NMT were predicted using scores in six simulation tests. Second, the admission batch was predicted by using scores in six simulation tests and NMT. Third, the admission batch was predicted by using scores in six simulation tests and the estimated scores in NMT. In all experiments, SVMs were compared with neural networks (NNs). Experimental results show that SVMs are much more stable and have better prediction ability.