Because Support Vector Data Description (SVDD) fails in identifying the local geometric information, a new detection method, called One-class SVDD based on Local Patch (OCSVDD_LP), was proposed. First, the data was divided into many local patches. Then, each sample was reconstructed by using the corresponding local patch. Finally, the decision model was obtained through training on the reconstruction data with SVDD. The experimental results on the artificial data set demonstrate that OCSVDD_LP can not only capture the global geometric structure of the data set, but also uncover the local geometric information. Besides, the results on real-world data sets validate the effectiveness of the proposed method.