Noncoding　RNAs　(ncRNAs)　play　crucial　roles　in　many　cellular　life　activities　by　interacting　with　proteins.　Identification　of　ncRNA-protein　interactions　(ncRPIs)　is　key　to　understanding　the　function　of　ncRNAs.　Although　a　number　of　computational　methods　for　predicting　ncRPIs　have　been　developed,　the　problem　of　predicting　ncRPIs　remains　challenging.　It　has　always　been　the　focus　of　ncRPIs　research　to　select　suitable　feature　extraction　methods　and　develop　a　deep　learning　architecture　with　better　recognition　performance.　In　this　work,　we　proposed　an　ensemble　deep　learning　framework,　RPI-EDLCN,　based　on　a　capsule　network　(CapsuleNet)　to　predict　ncRPIs.　In　terms　of　feature　input,　we　extracted　the　sequence　features,　secondary　structure　sequence　features,　motif　information,　and　physicochemical　properties　of　ncRNA/protein.　The　sequence　and　secondary　structure　sequence　features　of　ncRNA/protein　are　encoded　by　the　conjoint　k-mer　method　and　then　input　into　an　ensemble　deep　learning　model　based　on　CapsuleNet　by　combining　the　motif　information　and　physicochemical　properties.　In　this　model,　the　encoding　features　are　processed　by　convolution　neural　network　(CNN),　deep　neural　network　(DNN),　and　stacked　autoencoder　(SAE).　Then　the　advanced　features　obtained　from　the　processing　are　input　into　the　CapsuleNet　for　further　feature　learning.　Compared　with　other　state-of-the-art　methods　under　5-fold　cross-validation,　the　performance　of　RPI-EDLCN　is　the　best,　and　the　accuracy　of　RPI-EDLCN　on　RPI1807,　RPI2241,　and　NPInter　v2.0　data　sets　was　93.8%,　88.2%,　and　91.9%,　respectively.　The　results　of　the　independent　test　indicated　that　RPI-EDLCN　can　effectively　predict　potential　ncRPIs　in　different　organisms.　In　addition,　RPI-EDLCN　successfully　predicted　hub　ncRNAs　and　proteins　in　Mus　musculus　ncRNA-protein　networks.　Overall,　our　model　can　be　used　as　an　effective　tool　to　predict　ncRPIs　and　provides　some　useful　guidance　for　future　biological　studies.