Human　brain　network　analysis　based　on　machine　learning　has　been　paid　much　attention　in　the　field　of　neuroimaging,　where　the　application　of　convolutional　neural　network　(CNN)　is　now　becoming　a　new　research　hotspot.　However,　all　present　researches　based　on　conventional　CNN　share　weights　on　edges　connected　to　the　same　node　in　a　brain　network,　which　ignores　that　each　edge　between　any　two　nodes　has　a　unique　meaning　and　is　not　suitable　for　weight-sharing.　In　this　paper,　we　propose　a　new　convolutional　neural　network　with　elementwise　filters　(CNN-EW)　for　brain　networks.　More　specifically,　each　element-wise　filter　gives　a　unique　weight　to　each　edge　of　brain　network　which　may　reflect　the　topological　structure　information　more　realistically.　The　experimental　results　on　the　autism　brain　imaging　data　exchange　I　(ABIDE　I)　dataset　show　that　CNN-EW　models　can　not　only　more　accurately　distinguish　subject　groups　compared　to　some　fashionable　methods　but　also　identify　the　abnormal　brain　regions　associated　with　autism　spectrum　disorder　(ASD).