Although granular support vector machine (GSVM) can improve the learning speed, the generalization performance may be decreased because the original data distribution will be changed inevitably by two reasons: (1) A granule is usually replaced by individual datum; (2) Granulation and learning are carried out in different spaces. To address this problem, this study presents a granular support vector regression (SVR) model based on dynamical granulation, namely DGSVR, by using the dynamical hierarchical granulation method. With DGSVR, the original data are mapped into the high-dimensional space by mercer kernel to reveal the distribution features implicit in original sample space, and the data are divided into some granules initially. Then, some granules are obtained with important regression information by measuring the distances of granules and regression hyperplane. By computing the radius and density of granules, the deep dynamical granulation process executes until there are no informational granules need to be granulated. Finally, those granules in different granulation levels are extracted and trained by SVR. The experimental results on benchmark function datasets and UCI regression datasets demonstrate that the DGSVR model can quickly finish the dynamical granulation process and is convergent. It concludes this model can improve the generalization performance and achieve high learning efficiency at the same time.