Finding　reliable　miRNA　markers　and　revealing　their　potential　mechanisms　will　play　an　important　role　in　the　diagnosis　and　treatment　of　NSCLC.　Most　existing　computational　methods　for　identifying　miRNA　biomarkers　only　consider　the　expression　variation　of　miRNAs　or　rely　heavily　on　training　sets.　These　deficiencies　lead　to　high　false-positive　rates.　The　independent　regulatory　model　is　an　important　complement　to　traditional　models　of　co-regulation　and　is　more　impervious　to　the　dataset.　In　addition,　previous　studies　of　miRNA　mechanisms　in　the　development　of　non-small　cell　lung　cancer　(NSCLC)　have　mostly　focused　on　the　post-transcriptional　level　and　did　not　distinguish　between　NSCLC　subtypes.　For　the　above　problems,　we　improved　mainly　in　two　areas:　miRNA　identification　based　on　both　the　NOG　network　and　biological　functions　of　miRNA　target　genes;　and　the　construction　of　a　4-node　directed　competitive　regulatory　network　to　illustrate　the　mechanisms.　NSCLC　was　classified　as　lung　adenocarcinoma　(LUAD)　and　lung　squamous　cell　carcinoma　(LUSC)　in　this　work.　One　miRNA　biomarker　of　LUAD　(miR-708-5p)　and　four　of　LUSC　(miR-183-5p,　miR-140-5p,　miR-766-5p,　and　miR-766-3p)　were　obtained.　They　were　validated　using　literature　and　external　datasets.　The　ceRNA-hub-FFL　involving　transcription　factors　(TFs),　microRNAs　(miRNAs),　mRNAs,　and　long　non-coding　RNAs　(lncRNAs)　was　constructed.　There　were　multiple　interactions　among　these　components　within　the　net　at　the　transcriptional,　post-transcriptional,　and　protein　levels.　New　regulations　were　revealed　by　the　network.　Meanwhile,　the　network　revealed　the　reasons　for　the　previous　conflicting　conclusions　on　the　roles　of　CD44,　ACTB,　and　ITGB1　in　NSCLC,　and　demonstrated　the　necessity　of　typing　studies　on　NSCLC.　The　novel　miRNA　markers　screening　method　and　the　4-node　directed　competitive　ceRNA-hub-FFL　network　constructed　in　this　work　can　provide　new　ideas　for　screening　tumor　markers　and　understanding　tumor　development　mechanisms　in　depth.