3D shape segmentation is a fundamental computer vision task that partitions the object into labeled semantic parts. Recent approaches to 3D shape segmentation learning heavily rely on high-quality labeled training datasets. This limits their use in applications to handle the large scale unannotated datasets. In this paper, we proposed a novel semi-supervised approach, named Robust Learning of OneShot 3D Shape Segmentation (ROSS), which only requires one single exemplar labeled shape for training. The proposed ROSS can generalize its ability from a one-shot training process to predict the segmentation for previously unseen 3D shape models. The proposed ROSS is composed of three major modules for 3D shape segmentation as follows. The global shape descriptor generator is the first module that utilizes the proposed reference weighted convolution to learn a 3D shape descriptor. The second module is a part-aware shape descriptor constructor that can generate weighted descriptors from a learned 3D shape descriptor according to semantic parts without supervision. The shape morphing with label transferring works as the last module. It morphs the exemplar shape and then transfers labels from the transformed exemplar shape to the target shape. The extensive experimental results on 3D mesh datasets demonstrate the ROSS is robust to noise and incomplete shapes and it can be applied to unannotated datasets. The experiment shows the proposed ROSS can achieve comparable performance with the supervised method.