Abstract: Aiming at the problem of Radio Frequency Interference (RFI) in the process of radio astronomy observation, the RFI mitigation strategies of domestic and foreign stations are analyzed in detail. According to the RFI problems encountered in the actual observation process of each observatory, the prevention strategies and mitigation methods of RFI are studied from the aspects of active prevention stage, pre-correlation stage, post-correlation stage, machine learning and deep learning. The methods that can be adopted in the active prevention stage, adaptive filtering in the pre-correlation stage, spatial filtering method, VarThreshold, SumThreshold and singular value decomposition in the post correlation stage are systematically analyzed. The applications of principal component analysis based on machine learning, support vector machine, full convolution neural network, convolution neural network, U-Net and other related technologies and methods in RFI signal processing are discussed.