Drug-target interaction prediction can help researchers understand the mechanism of action of drugs and discover new drug targets, and even assist researchers in designing more effective drug therapeutic regimens, which will be of great significance to drug development. In recent years, computer-aided technology has been better applied in various fields, and the paper will discuss drug-target interaction prediction methods: molecular docking, ligand-based, text mining, and feature-based methods in the context of computer-aided technology. The paper mainly discusses and elaborates on the feature-based techniques and analyzes each type of method’s principles, advantages, and disadvantages. At the same time, the paper also discusses the problems and challenges faced by drug-target interaction prediction, including its dataset, cold-start, and model design problems. Finally, deep learning technology will still play a great potential for application in this field and briefly point out the direction and trend of future research.