ncRNA-protein　interactions　(ncRPIs)　play　an　important　role　in　a　number　of　cellular　processes,　such　as　post-transcriptional　modification,　transcriptional　regulation,　disease　progression　and　development.　Since　experimental　methods　are　expensive　and　time-consuming　to　identify　the　ncRPIs,　we　proposed　a　computational　method,　Deep　Mining　ncRNA-Protein　Interactions　(DM-RPIs),　for　identifying　the　ncRPIs.　In　order　to　descending　dimension　and　excavating　hidden　information　from　k-mer　frequency　of　RNA　and　protein　sequences,　using　the　Deep　Stacking　Auto-encoders　Networks　(DSANs)　model　refined　the　raw　data.　Three　common　machine　learning　algorithms,　Support　Vector　Machine　(SVM),　Random　Forest　(RF),　and　Convolution　Neural　Network　(CNN),　were　separately　trained　as　individual　predictors　and　then　the　three　individual　predictors　were　integrated　together　using　stacked　ensembling　strategy.　Based　on　the　RPI2241　dataset,　DM-RPI　obtains　an　accuracy　of　0.851,　precision　of　0.852,　sensitivity　of　0.873,　specificity　of　0.826,　and　MCC　of　0.701,　which　is　promising　and　pioneering　for　the　prediction　of　ncRPIs.