Accumulating　evidence　has　demonstrated　that　RNAs　play　an　important　role　in　identifying　various　complex　human　diseases.　However,　the　number　of　known　disease　related　RNAs　is　still　small　and　many　biological　experiments　are　time-consuming　and　labor-intensive.　Therefore,　researchers　have　focused　on　developing　useful　computational　algorithms　to　predict　associations　between　diseases　and　RNAs.　It　is　useful　for　people　to　identify　complex　human　diseases　at　molecular　level,　especially　in　diseases　diagnosis,　therapy,　prognosis　and　monitoring.　In　this　paper,　we　propose　a　novel　framework　Graph　Convolutional　Attention　Network(GCAN)　to　predict　potential　disease-RNAs　associations.　Facing　thousands　of　associations,　GCAN　benefits　from　the　efficiency　of　deep　learning　model.　Compared　to　other　disease-RNAs　association　prediction　methods,　GCAN　operates　the　computation　process　from　global　structure　of　disease-RNAs　network　with　graph　convolution　networks(GCN)　and　can　also　integrate　local　neighborhoods　with　the　attention　mechanism.　What　is　more,　GCAN　is　at　the　first　attempt　to　utilize　GCN　to　discover　the　feature　representation　of　the　latent　nodes　in　disease-RNAs　network.　In　order　to　evaluate　the　performance　of　GCAN,　we　conduct　experiments　on　two　different　disease-RNAs　networks:　disease-miRNA　and　disease-lncRNA.　Comparisons　of　several　state-of-the-art　methods　using　disease-RNAs　networks　show　that　our　novel　frameworks　outperform　baselines　by　a　wide　margin　in　potential　disease-RNAs　associations.　©　2019　IEEE.