Despite　the　tremendous　progress　in　molecular　analysis　of　pan-cancer,　little　is　known　regarding　molecular　classification　of　cervical　squamous　cell　carcinoma.　In　this　study,　we　adopted　a　multi-omics　approach　to　identify　potential　key　classification　features　of　cervical　squamous　cell　carcinoma.　Specifically,　we　analyzed　mRNA,　and　microRNA　(miRNA)　expression　data,　as　well　as　DNA　methylation　and　copy　number　variation　in　cervical　squamous　cell　carcinoma　cases,　using　datasets　obtained　from　The　Cancer　Genome　Atlas　(TCGA).　Moreover,　we　identified　molecules　in　each　dimension,　as　well　as　integrated　and　clustered　filtered　classification　features,　and　used　them　to　distinguish　different　subtypes.　The　resulting　key　classification　features　were　used　to　establish　a　classification　model　for　cervical　squamous　cell　carcinoma.　Our　results　revealed　two　cervical　squamous　cell　carcinoma　subtypes,　with　significant　differences　across　clinical　survival　levels,　as　well　as　8　key　classification　features　of　cervical　squamous　cell　carcinomas.　These　findings　are　expected　to　provide　important　references　for　early　classification　of　cervical　squamous　cell　carcinoma　and　identification　of　classification　markers.