Molecular classification of cancer is the current frontier of cancer omics and tumor precision medicine. Although great progress has been made in molecular analysis of whole cancer, the molecular classification of cervical squamous cell carcinoma still needs more exploration. In order to find the potential subtypes of cervical squamous cell carcinoma, this paper proposed a data processing and analysis process based on the classification of cancer subtypes based on multi-omics data. 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. 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.