This paper presents a data-driven method for rapid 3D garment modeling, which can efficiently generate new garments by examples. First, a semantic segmentation is applied to simultaneously segment and label the components in given 3D garments using shape analysis of garment styles. Second, the garment components are clustered into a garment component library with four categories including tops, bottoms, sleeves, and accessories. Third, a continuous style description of the 3D garments, characterized by the ratio of area and boundary length, is constructed to recommend components that can be regarded as a new 3D garment. The final new suitable garments are produced by optimizing two component meshes. Experimental results show that the presented method is able to satisfy the new requirements of online 3D model collections while ensuring the efficiency of 3D garment modeling.