Hepatocellular　carcinoma　(HCC)　is　one　of　the　malignancies　with　high　morbidity　and　mortality　in　the　world.　The　purpose　of　our　study　was　to　search　for　HCC　related　miRNA　prognostic　biomarkers　to　predict　the　risk　degree　and　survival　time　of　HCC　patients　and　provide　effective　prognostic　information　for　HCC　patients.　Four　methods　were　used　to　identify　differentially　expressed　miRNAs　(DEMs)　from　The　Cancer　Genome　Atlas(TCGA).　Kaplan-Meier　survival　curve,　univariate　and　multivariable　Cox　regression　analysis　were　used　to　identify　prognostic　miRNAs　of　HCC　from　DEMs.　Four　prognostic　miRNAs　biomarkers　(hsa-miR-132-3p,　hsa-miR-139-5p,　hsa-miR-3677-3p,　hsa-miR-500a-3p)　of　HCC　were　identified　at　last,　and　combined　into　a　risk　score　model.　There　was　no　experimental　evidence　that　hsa-mir-3677-3p　is　related　to　HCC,　and　it　was　a　newly　discovered　miRNA　in　this　study.　The　evaluation　results　of　various　bioinformatics　methods,　including　survival　curve,　ROC　curve,　chi-square　test,　et　al..　All　indicated　that　the　risk　score　calculated　by　the　model　can　effectively　predict　the　risk　degree　of　patients(P＜0.000,　hazard　ratio=2.551,　95%　confidence　interval=1.751-3.717).　1-5　year　survival　rates　of　HCC　patients　in　the　low　risk　group　had　20%-30%　higher　than　in　the　high　risk　group.　Through　the　clinical　data　analysis,　it　was　found　that　the　combined　biomarkers　have　a　better　prognostic　effect　than　other　clinical　indicators,　and　can　also　be　used　as　an　independent　prognostic　factor.　Target　genes　of　four　miRNAs　were　predicted,　including　AGO2,　FOXO1,　ROCK2,　RAP1B,　CYLD,　et　al.,　and　enriched　in　biological　processes　such　as　cell　proliferation,　migration,　apoptosis　and　immune　response.