Graph convolutional networks (GCNs) successfully generalize convolutional neural networks to handle the graphs with high-order arbitrary structures. However, most existing GCNs variants consider only the local geometry of row vectors of high-dimensional data via example graph Laplacian, while neglecting the manifold structure information of column vectors. To address this problem, we propose the example-feature graph convolutional networks (EFGCNs) for semi-supervised classification. Particularly, we introduce the definition of the spectral example-feature graph (EFG) convolution that simultaneously utilizes the example graph Laplacian and feature graph Laplacian to better preserve the local geometry distributions of data. After optimizing the spectral EFG convolution with the first-order approximation, a single-layer EFGCNs is obtained. It is then further extended to build a multi-layer EFGCNs. Extensive experiments on remote sensing and citation networks datasets demonstrate the proposed EFGCNs show superior performance in semi-supervised classification compared with state-of-the-art methods.