This paper introduces SfmCAD, a novel unsupervised network that learns the Sketch-based Feature Modeling operations used in modern CAD workflow to reconstruct 3D shapes. Given a 3D shape represented as voxels, SfmCAD learns a neural typed sketch+path representation, including 2D sketches of feature primitives and their 3D sweeping paths without supervision, for inferring feature-based CAD programs. This approach bridges the gap between detail-oriented shape reconstruction and the simplicity and control intrinsic to primitive extraction. By utilizing 2D sketches to represent local shape details and sweeping paths to encapsulate the structure of the shape, SfmCAD achieves an interpretable decoupling of shape structure and local details. By manipulating the parametric 2D sketch and 3D path, SfmCAD facilitates users in making distinct modifications to both the geometric and structural characteristics of the shape. We demonstrate the effectiveness of our method by applying SfmCAD to many different types of objects, such as CAD parts, ShapeNet objects, and tree shapes. Extensive comparisons show that SfmCAD produces compact and faithful 3D reconstructions with superior quality than existing alternatives. The code will be released to facilitate future research.