In this paper, we propose structural features for spatial graph convolution to classify signals on graphs. Existing graph convolution methods are limited to utilize the structural information of surrounding neighboring nodes for a target node in feature space. Graph convolution performances will be improved if it can fully utilize the structural information. To achieve this goal, we first define three structural features for characterizing the structure of surrounding neighboring nodes, i.e., feature angle, feature distance, and relationship embedding. We then concatenate the features and perform graph convolution by aggregating and integrating them. We use this graph convolution algorithm for the basis of graph neural networks for classification of 3D point clouds and nodes in a citation network. Through experiments, our approach presents higher classification accuracies than existing methods.