Objective: To predict the prognosis of cervical cancer, we constructed a novel model with 5 specific cell types and identified a potential biomarker. Methods: We employed CIBERSORT and xCell method to evaluate the abundances of 23 cells types in tumor microenvironment. Five specific cell types were filtrated to determine different immunotypes by applying least absolute shrinkage and selection operator (LASSO) Cox regression method. The expression of immune checkpoints (ICPs) and effectors were validated by immunohistochemistry. Correlation analysis was performed to examine the relevance between PIK3CA mutational status and ICPs. Results: Unsupervised clustering of patients on the basis of tumor infiltrating lymphocytes and fibroblasts identified patients with shorter overall survival (OS) (hazard ratio [HR]=3.0729; 95% confidence interval [CI]=1.5103–6.2522; p=0.0118). An immunoscore (IS) signature consisting of 5 immune cell types infiltrating in tumor core (CD8T, activated NK cells, neutrophils, activated mast cells, macrophages) was constructed using LASSO Cox regression analysis. Receiver operating characteristic curves confirmed that the area under the curve of IS was significantly higher to that of International Federation of Gynecology and Obstetrics staging alone (0.637 vs. 0.55). Survival analysis revealed patients in high IS group exhibited a poorer OS (HR=3.0113; 95% CI=1.8746–4.8373; p<0.0001). The multivariate analysis indicated the IS was an independent prognostic factor. In addition, the lower IS related to higher expression of ICPs and neoantigen load. Conclusions The identification of IS in cervical cancer tissues could facilitate patient risk stratification and selection of immunotherapeutic responses, but more prospective studies are needed to assess its reliability.