Abstract: To overcome the problem of over-fitted caused by noises and outliers in support vector regress（SVR），weighted coefficient model based on support vector data description（SVDD） is used in this paper.The weighted coefficient value to each input sample is confirmed according to its distance to the center of the smallest enclosing hyper-sphere in the feature space.The proposed model is applied to weighted support vector regression（WSVR） for 1-dimensional data set simulation.The results indicate that the proposed method actually reduces the error of regression and yields higher accuracy than support vector regression（SVR）does.