Background:　The　aberrant　expressions　of　lncRNAs　have　been　frequently　demonstrated　to　be　closely　associated　with　the　prognosis　of　patients　in　many　cancer　types　including　hepatocellular　carcinoma　(HCC).　Integration　of　these　lncRNAs　might　provide　accurate　evaluation　of　HCC.　Therefore,　this　study　aims　to　develop　a　novel　prognostic　evaluation　model　based　on　the　expression　of　lncRNAs　to　predict　the　survival　of　HCC　patients,　postoperatively.　Patients　and　Methods:　RNA　sequencing　(RNA-seq)　analysis　was　performed　for　61　HCC　patients　(training　cohort)　to　screen　prognosis-associated　lncRNAs　with　univariate　Cox　regression　and　Log　rank　test　analyses.　Multivariate　Cox　regression　analysis　was　then　applied　to　establish　the　final　model,　which　was　further　verified　in　a　validation　cohort　(n=191).　Moreover,　performance　of　the　mode　was　assessed　with　time-dependent　receiver　operating　characteristic　curve　(tdROC),　Harrell＇s　c-index,　and　Gonen　&　Heller＇s　K.　Results:　After　a　serial　statistical　computation,　a　novel　risk　scoring　model　consisting　of　four　lncRNAs　and　TNM　staging　was　established,　which　could　successfully　divide　the　HCC　patients　into　low-risk　and　high-risk　groups　with　significantly　different　OS　and　RFS　in　both　training　and　validation　cohorts.　tdROC　analysis　showed　that　this　model　achieved　a　high　performance　in　predicting　OS　and　2-year　RFS　in　both　cohorts.　Gene　Set　Enrichment　Analysis　revealed　that　HCC　tumor　tissues　with　high-risk　score　have　stronger　capacities　in　immune　escape　and　resistance　to　treatment.　Conclusion:　We　successfully　established　a　novel　prognostic　evaluation　model,　which　exhibited　reliable　capacity　in　predicting　the　OS　and　early　recurrence　of　HCC　patients　with　relatively　higher　accuracy.