It　has　been　widely　known　that　long　non-coding　RNA　(lncRNA)　plays　an　important　role　in　gene　expression　and　regulation.　However,　due　to　a　few　characteristics　of　lncRNA　(e.g.,　huge　amounts　of　data,　high　dimension,　lack　of　noted　samples,　etc.),　identifying　key　lncRNA　closely　related　to　specific　disease　is　nearly　impossible.　In　this　paper,　the　authors　propose　a　computational　method　to　predict　key　lncRNA　closely　related　to　its　corresponding　disease.　The　proposed　solution　implements　a　BPSO　based　intelligent　algorithm　to　select　possible　optimal　lncRNA　subset,　and　then　uses　ML-ELM　based　deep　learning　model　to　evaluate　each　lncRNA　subset.　After　that,　wrapper　feature　extraction　method　is　used　to　select　lncRNAs,　which　are　closely　related　to　the　pathophysiology　of　disease　from　massive　data.　Experimentation　on　three　typical　open　datasets　proves　the　feasibility　and　efficiency　of　our　proposed　solution.　This　proposed　solution　achieves　above　93%　accuracy,　the　best　ever.