In this paper,we propose a SVM-combined generative statistical model for Chinese dependency analysis that trains SVM classifier using erroneous results generated by generative statistical model.To further improve the precision of dependency analysis,two measures were taken,first,dynamic programming algorithm that extends the range of finding the best local solution was used to estimate the error rate of generative model;second,a ranging factor was introduced to make the solutions adaptive on the practical situation.All those efforts make it possible for the new method to largely decrease the number of negative support vectors without sacrificing classification ability in training.Comparative experiments on Hit Chinese Treebank corpus show that the new method shows better performance than current Chinese dependency methods,with precision reaching to 86.4%.