Aiming at the problem that the accuracy and recall of the protein complexes identification algorithm based on ant colony and FCM clustering are not high and the running efficiency is low, this paper proposed a novel protein complex recognition algorithm named FAC-PC. Firstly, combing with the Pearson correlation coefficient and edge aggregation coefficient, the algorithm constructed the weighted protein network. Secondly, in order to overcome the defects of massive merger and filter, repeated pick-up and drop-down operations in ant colony clustering algorithm, it designed the EPS metric to select essential protein, and designed the PFC metric to traverse neighbors of essential proteins to obtain essential group proteins. Then it used the essential group protein to replace the seed node in the process of ant colony clustering, which resulted that the accuracy and time performance were improved. Furthermore, it proposed the SI metric to optimize the probability of pick-up and drop-down operations of ant colony to obtain the number of clustering. Finally, according to the improved ant colony algorithm, it obtained the essential protein and the number of clustering to initialize the FCM algorithm, and designed the membership update strategy to optimize the membership update, at the same time, it proposed a new FCM objective function which took a ba-lance between intra-clustering and inter-clustering variation, and finally identified the protein complex by improved FCM algorithm. This paper used FAC-PC algorithm to identify protein complexes on DIP data. The experimental results show that FAC-PC algorithm has better performance on accuracy and recall, which is more reasonable to identify protein complexes.