Non-coding　RNA　(ncRNA)　plays　an　important　role　in　many　fundamental　biological　processes,　and　it　may　be　closely　associated　with　many　complex　human　diseases.　NcRNAs　exert　their　functions　by　interacting　with　proteins.　Therefore,　identifying　novel　ncRNA-protein　interactions　(NPIs)　is　important　for　understanding　the　mechanism　of　ncRNAs　role.　The　computational　approach　has　the　advantage　of　low　cost　and　high　efficiency.　Machine　learning　and　deep　learning　have　achieved　great　success　by　making　full　use　of　sequence　information　and　structure　information.　Graph　neural　network　(GNN)　is　a　deep　learning　algorithm　for　complex　network　link　prediction,　which　can　extract　and　discover　features　in　graph　topology　data.　In　this　study,　we　propose　a　new　computational　model　called　GATLGEMF.　We　used　a　line　graph　transformation　strategy　to　obtain　the　most　valuable　feature　information　and　input　this　feature　information　into　the　attention　network　to　predict　NPIs.　The　results　on　four　benchmark　datasets　show　that　our　method　achieves　superior　performance.　We　further　compare　GATLGEMF　with　the　state-of-the-art　existing　methods　to　evaluate　the　model　performance.　GATLGEMF　shows　the　best　performance　with　the　area　under　curve　(AUC)　of　92.41%　and　98.93%　on　RPI2241　and　NPInter　v2.0　datasets,　respectively.　In　addition,　a　case　study　shows　that　GATLGEMF　has　the　ability　to　predict　new　interactions　based　on　known　interactions.　The　source　code　is　available　at　https://github.com/JianjunTan-Beijing/GATLGEMF.