Due to the noise in protein-protein interaction(PPI) network, the poor efficiency of detecting process, as well as the poor identification accuracy of essential proteins, this paper proposed a method named PCSL(United Protein Complexes and Subcellular Locallizations) based on protein complex information and subcellular locallizations to identify essential proteins. Firstly, this method integrate topological data, biological data and subcellular locallization data to construct weighted network to reduce the noise(the false positive and the false negative) impact in the original PPI network. Secondly, according to the complex property and space property of essential proteins, this paper designed a measure to measure the essentiality of proteins from weighted network, which emphasize the importance of the essential proteins fromSmulti-dimension angle. Finally, based on CPPK algorithm, a new probe strategy is designed to improve the efficiency of detecting essential proteins from weighted network. This paper applied PCSL method to the DIP dataset for predicting essential proteins. Compared with other ten methods of predicting essential proteins, the experimental results show that this method can identify more essential proteins and have a better performance on predicting essential proteins.