Background:　Non-coding　RNA　(ncRNA)　and　protein　interactions　play　essential　roles　in　various　physiological　and　pathological　processes.　The　experimental　methods　used　for　predicting　ncRNA-protein　interactions　are　time-consuming　and　labor-intensive.　Therefore,　there　is　an　increasing　demand　for　computational　methods　to　accurately　and　efficiently　predict　ncRNA-protein　interactions.　Results:　In　this　work,　we　presented　an　ensemble　deep　learning-based　method,　EDLMFC,　to　predict　ncRNA-protein　interactions　using　the　combination　of　multi-scale　features,　including　primary　sequence　features,　secondary　structure　sequence　features,　and　tertiary　structure　features.　Conjoint　k-mer　was　used　to　extract　protein/ncRNA　sequence　features,　integrating　tertiary　structure　features,　then　fed　into　an　ensemble　deep　learning　model,　which　combined　convolutional　neural　network　(CNN)　to　learn　dominating　biological　information　with　bi-directional　long　short-term　memory　network　(BLSTM)　to　capture　long-range　dependencies　among　the　features　identified　by　the　CNN.　Compared　with　other　state-of-the-art　methods　under　five-fold　cross-validation,　EDLMFC　shows　the　best　performance　with　accuracy　of　93.8%,　89.7%,　and　86.1%　on　RPI1807,　NPlnter　v2.0,　and　RP1488　datasets,　respectively.　The　results　of　the　independent　test　demonstrated　that　EDLMFC　can　effectively　predict　potential　ncRNA-protein　interactions　from　different　organisms.　Furtherly,　EDLMFC　is　also　shown　to　predict　hub　ncRNAs　and　proteins　presented　in　ncRNA-protein　networks　of　Mus　musculus　successfully.　Conclusions:　In　general,　our　proposed　method　EDLMFC　improved　the　accuracy　of　ncRNA-protein　interaction　predictions　and　anticipated　providing　some　helpful　guidance　on　ncRNA　functions　research.　The　source　code　of　EDLMFC　and　the　datasets　used　in　this　work　are　available　at　https://github.com/JingjingWang-87/EDLMFC.