Abstract: This study used bioinformatics methods to screen highly expressed protein kinases and their inhibitors, which may provide references for molecular targeted therapy in cervical cancer. Expression profile dataset GSE63514 was downloaded from GEO database, and the highly expressed protein kinase genes were identified. GO enrichment and KEGG pathway analysis were conducted with clusterProfiler package. The most selectivity inhibitors of highly expressed kinases were selected based on kinase-kinase inhibitor interaction map and eva-luated them with literature mining. A total of 1 167 differentially expressed genes were identified from the expression profile dataset, of which 33 were highly expressed kinase genes. GO analysis showed that these genes were mainly enriched in protein phosphorylation, cell cycle, DNA damage and other biological processes. KEGG pathway analysis indicated that these genes were enriched in cell cycle, p53 signaling pathway, oocyte meiosis and human papilloma virus（HPV） infection pathways. Based on the kinase-kinase inhibitor interaction map, the most potent 8 highly expressed kinases correspond to 16 most selective kinase inhibitors were identified. The results of the literature mining showed that the 16 inhibitors have been studied in tumors, but 8 of them have no literature associations with cervical cancer and have potential to become new drugs against cervical cancer, which might provide new references for cervical cancer targeted therapy.