The marked improvements in deep learning influence almost every area of computer sciences. The mesh segmentation in computer graphics has been an active research area and keep abreast of the trend of deep learning developments. The mesh segmentation has a central role in multiple application areas for 3D objects. It is chiefly used to produce the object structure in order to manipulate the object or analyze the components of it. These operations are primitive, and that primitiveness causes a variety of application areas. The variation in application areas induce a variety of priority deviations over time and memory usage. In this thesis, we solve the mesh segmentation problem by using Graph Convolutional Neural Networks. Our method uses a semi-supervised approach for which the mesh objects are sparsely labeled, and the results are the formed segments. We consider a mesh object as a graph by using their connectedness over the faces, and having the mesh in 3D lets us create geometrically logical features for our network. Using the neighborhood information is maintained by the Graph Convolutional Neural Networks, which is a pretty new concept, and the application on the sparsely labeled mesh segmentation is novel to our work. By using the briefly summarized method, we reach competitive results compared to state-of-art mesh segmentation methods.