Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins. In EPCRO, the naive Bayes model is employed to analyze the homogeneity between proteins. In order to improve the identification rate and speed of essential proteins, the protein homogeneity rate is integrated into the CRO algorithm to balance between local and global searches. EPCRO is experimentally compared with 17 existing methods (including, DC, SC, IC, EC, LAC, NC, PeC, WDC, EPD-RW, RWHN, TEGS, CFMM, BSPM, AFSO-EP, CVIM, RWEP, and EPPSODC) based on biological datasets. The results show that EPCRO is superior to the above methods in identification accuracy and speed.