Graph　convolutional　neural　networks　are　designed　to　apply　convolutional　operations　directly　on　non-Euclidean　structure　graph　data,　generating　orderly　arranged　matrix　representations　of　graphs.　However,　only　node　features　are　fully　exploited　even　though　edge　features　may　also　play　an　important　role　in　some　domains　such　as　chemoinformatics.　In　this　paper,　we　proposed　two　new　approaches　of　utilizing　edge　features　on　graph　convolutional　neural　networks,　Feature　embedding　adjacent　matrix　and　Reverse　graph.　Methodologies　of　basic　graph　convolutional　neural　networks　only　tend　to　propagate　node　features　to　neighbor　nodes　along　edges　by　convolutional　operations.　By　applying　Feature　embedding　adjacent　matrix,　edge　features　are　synthesized　into　node　features　and　also　propagated　to　neighbor　nodes　during　propagation　process.　Reverse　graph　approach　builds　a　special　auxiliary　graph　to　propagate　edge　features　to　neighbor　edges.　Therefore,　a　synthetical　presentation　including　both　edge　features　and　node　features　is　built.　Experiments　demonstrated　our　new　approaches　improve　graph　classification　accuracies,　especially　on　data　sets　with　low　accuracies　on　basic　GCNs.