Background　Diffuse　large　B-cell　lymphoma　(DLBCL)　exhibits　remarkable　heterogeneity　but　still　remains　undiagnosed　in　identifying　the　subpopulation　of　DLBCL　to　predict　the　prognosis　and　guide　clinical　treatment.Methods　Molecular　subgroups　were　identified　in　gene　expression　data　from　GSE10846　by　a　consensus　clustering　algorithm.　And　gene　set　enrichment　analysis,　immune　infiltration,　and　the　proposed　cell　cycle　algorithm　were　applied　to　explore　the　biological　functions　of　different　subtypes.　Meanwhile,　univariate　and　multivariate　Cox　regression　analyses　were　used　to　evaluate　independent　prognostic　factors　of　DLBCL.　Finally,　the　prognostic　model,　including　some　key　genes　screened　by　Lasso　regression,　Random　Forest　algorithm,　and　point-biserial　correlation,　was　constructed　by　an　optimal　classifier　from　seven　machine　learning　algorithms　and　validated　by　another　three　external　datasets　(GSE34171,　GSE87371,　GSE31312).ResultsComprehensive　genomic　analysis　of　1,143　DLBCL　samples　identify　2　molecularly,　prognostically　relevant　subtypes:　immune-enriched　(IME)　and　cell-cycle-enriched　(CCE).　Then　a　new　predictive　model　including　seven　key　genes　(SERPING1,　TIMP2,　NME1,　DCTPP1,　RFC4,　POLE2,　and　SNRPD1)　was　developed　with　high　prediction　accuracy　(88.6%)　and　strong　predictive　power　(AUC　=　0.973)　based　on　the　Support　Vector　Machine　(SVM)　algorithm　in　414　patients　from　GSE10846.　The　predictive　power　was　similar　in　another　three　testing　sets　(HR　＞　1.400,　p　＜　0.05).Conclusion　This　model　could　evaluate　survival　independently　with　strong　predictive　power　compared　with　other　clinical　risk　factors.　Our　study　constructed　a　reliable　model　to　predict　two　new　subtypes　of　DLBCL　patients,　which　could　guide　the　implementation　of　individualized　treatment.