The effect of classification learning is closely related to the distribution of limited training samples. Support vector data description （SVDD）， as a single boundary solution model， cannot well describe the actual distribution characteristics of the data， resulting in some target objects falling outside the hypersphere. To improve its classification ability， this paper proposes a granular computing-driven SVDD （GrC-SVDD） classification method to construct a multi-granularity levels attribute sets and the corresponding multi-granular hyperspheres. Firstly，the importance of the attribute within the current granularity level is calculated through the neighborhood self-information. Secondly， the best attribute set is then chosen to retrain the hyperspheres that did not achieve the purity criterion at the previous granularity level， and so on until all hyperspheres meet the conditions or the attributes are exhausted. The experimental section discusses the effect of parameters on classification performance and learns hyperparameters. The experimental results show that GrC-SVDD has better classification performance compared with SVDD and popular classification methods.