Motivation: It was found that high accuracy splicing-site recognitio n of rice (Oryza sativa L.) DNA sequence is especially difficult. We describe d a new method for the splicing-site recognition of rice DNA sequences. Method: Bas e d on the intron in eukaryotic organisms conforming to the principle of GT-AG,w e used support vector machines (SVM) to predict the splicing sites. By machine l earning,we built a model and used it to test the effect of the test data set of true and pseudo splicing sites. Results: The prediction accuracy we obtained wa s 87.53% at the true 5’ end splicing site and 87.37% at the true 3’ end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches. Motivation: It was found that high accuracy splicing-site recognitio n of rice (Oryza sativa L.) DNA sequence is more difficult. We describe da new method for the splicing-site recognition of rice DNA sequences. Method: Bas ed on the intron in eukaryotic organisms conforming to the principle of GT-AG, we used support vector machines (SVM) to predict the splicing sites. By machine l earning, we built a model and used it to test the effect of the test data set of true and Results: The prediction accuracy of obtained wa s 87.53% at the true 5 ’end splicing site and 87.37% at the true 3’ end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches .