At　present,　most　of　the　researches　on　protein　classification　are　based　on　graph　kernels.　The　essence　of　graph　kernels　is　to　extract　the　substructure　and　use　the　similarity　of　substructures　as　the　kernel　values.　In　this　paper,　we　propose　a　novel　graph　kernel　named　vertex-edge　similarity　kernel　(VES　kernel)　based　on　mixed　matrix,　the　innovation　point　of　which　is　taking　the　adjacency　matrix　of　the　graph　as　the　sample　vector　of　each　vertex　and　calculating　kernel　values　by　finding　the　most　similar　vertex　pair　of　two　graphs.　In　addition,　we　combine　the　novel　kernel　with　the　neural　network　and　the　experimental　results　show　that　the　combination　is　better　than　the　existing　advanced　methods.