The identification of protein complexes is significant to understand the mechanisms of cellular processes.Up to now,many methods have been developed to identify protein complexes in static PPI networks.However,static PPI networks cannot accurately describe the behaviors of proteins in the different stages of life cycle of a cell.In this paper,we integrate gene expression data and GO terms into high-throughput PPI data to construct weighted dynamic PPI networks,on which we propose a new method to detect protein complexes.Specifically,we first calculate protein active probability and protein functional similarity to construct weighted dynamic PPI networks,then define a high-order topological overlap measure of similarity to extract protein complexes based on the core-attachment model.In our experiments,four PPI datasets are used to detect protein complexes.Experimental results indicate that our method is superior to the existing methods in overall.