The　heterogeneity　of　Hepatocellular　Carcinoma　(HCC)　poses　a　barrier　to　effective　treatment.　Stratifying　highly　heterogeneous　HCC　into　molecular　subtypes　with　similar　features　is　crucial　for　personalized　anti-tumor　therapies.　Although　driver　genes　play　pivotal　roles　in　cancer　progression,　their　potential　in　HCC　subtyping　has　been　largely　overlooked.　This　study　aims　to　utilize　driver　genes　to　construct　HCC　subtype　models　and　unravel　their　molecular　mechanisms.　Utilizing　a　novel　computational　framework,　we　expanded　the　initially　identified　96　driver　genes　to　1192　based　on　mutational　aspects　and　an　additional　233　considering　driver　dysregulation.　These　genes　were　subsequently　employed　as　stratification　markers　for　further　analyses.　A　novel　multi-omics　subtype　classification　algorithm　was　developed,　leveraging　mutation　and　expression　data　of　the　identified　stratification　genes.　This　algorithm　successfully　categorized　HCC　into　two　distinct　subtypes,　CLASS　A　and　CLASS　B,　demonstrating　significant　differences　in　survival　outcomes.　Integrating　multi-omics　and　single-cell　data　unveiled　substantial　distinctions　between　these　subtypes　regarding　transcriptomics,　mutations,　copy　number　variations,　and　epigenomics.　Moreover,　our　prognostic　model　exhibited　excellent　predictive　performance　in　training　and　external　validation　cohorts.　Finally,　a　10-gene　classification　model　for　these　subtypes　identified　TTK　as　a　promising　therapeutic　target　with　robust　classification　capabilities.　This　comprehensive　study　provides　a　novel　perspective　on　HCC　stratification,　offering　crucial　insights　for　a　deeper　understanding　of　its　pathogenesis　and　the　development　of　promising　treatment　strategies.　Dividing　highly　heterogeneous　HCC　into　molecular　subtypes　with　similar　characteristics　is　crucial　for　personalized　anti-tumor　therapies.　Although　driver　genes　play　pivotal　roles　in　cancer　progression,　their　potential　in　HCC　subtyping　has　been　largely　overlooked.　In　this　work,　we　developed　a　multi-omics　network-based　stratification　algorithm　that　utilizes　patient　mutation　data　and　requires　smaller　computational　resources　for　subtype　assignment.　Through　this　algorithm,　we　categorized　HCC　into　two　subtypes,　CLASS　A　and　CLASS　B.　Using　multi-omics　and　single-cell　data,　we　identified　differences　between　these　subtypes　in　gene　expression,　methylation,　immune　infiltration,　and　other　aspects.　Beyond　subtype　characterization,　our　study　established　a　robust　clinical　prediction　model　(https://mike-wang-bjut.shinyapps.io/DynNomapp_HCC_Sutypes/)　incorporating　subtype　information　and　typical　clinical　features,　enabling　precise　survival　predictions.　Finally,　we　developed　a　high-performing　machine　learning　classifier　for　our　subtype.　Analyzing　this　classification　model　and　reviewing　previous　experimental　papers,　we　identified　TTK　as　a　potential　diagnostic　marker　and　therapeutic　target　specific　to　our　subtypes.　In　conclusion,　our　research　offers　a　novel　perspective　on　HCC　stratification,　which　is　crucial　for　a　deeper　understanding　of　its　pathogenesis　and　developing　promising　treatment　strategies.