Point　cloud　data　classification　has　been　widely　used　in　autonomous　driving,　robot　perception,　and　virtual/augmented　reality.　Due　to　its　irregularity　and　disorder,　the　classification　task　of　point　clouds　needs　to　transform　the　point　cloud　into　a　multi-view　or　voxel　grid,　and　then　use　the　traditional　convolution　neural　network　processing.　However,　this　process　is　not　only　complex　in　operation　but　also　low　in　classification　accuracy.　To　solve　this　problem,　a　new　point　cloud　classification　method　based　on　the　graphical　convolutional　neural　network　(GCN)　is　proposed.　Firstly,　based　on　PointNet,　KNN　graph　is　introduced　to　obtain　global　deep　features.　Then　the　3D　point　cloud　is　represented　as　a　directed　graph,　local　features　are　extracted　by　edge　convolution.　Finally,　the　extracted　global　and　local　features　are　aggregated　to　represent　and　classify　point　clouds.　The　proposed　network　is　evaluated　on　the　open　dataset　ModelNet40　and　3DMNIST.　Experimental　results　show　that　the　proposed　network　can　achieve　on　par　or　better　performance　than　state-of-the-art,　such　as　PointNet,　PointNet++,　DGCNN,　and　PointCNN,　for　point　cloud　classification.