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Proteins, as the material basis of life, are the ultimate controller and direct performer of life activities. Most proteins perform their biological functions by binding to other proteins to form complexes. The identification of protein complexes can help people to better understand the organization and functions of the complexes, as well as the mechanisms of cells. At present, the rapid development of high-throughput experimental technology has led to huge amounts of protein-protein interaction (PPI) data. Many methods for computationally predicting protein complexes based on PPI data have been proposed. Different methods have their own characteristics and advantages, and they also have inherent drawbacks. Firstly, this paper classifies and comprehensively analyzes and reviews the existing protein complex prediction methods. Then, it introduces the commonly used evaluation indicators and main data sets in complex prediction, compares and analyzes the prediction performance of several representative methods. Finally, it summarizes state-of-the-art complex prediction methods, highlights future research directions, and puts forward several issues to be resolved in the future. It is hoped that through the analysis and comparison of various methods, this paper can provide some valuable guidance and future directions for users and researchers on using the existing methods and developing new methods of protein complex prediction.