Abstract: ‍ ‍Accurate identification of radar jamming signal is the premise of radar jamming， which is very important for radar survival. In view of the traditional radar jamming signal recognition method needs complicated analysis and calculation to extract features， and it is difficult to adapt to the complex radar working environment because of its poor universality and weak generalization ability. In this paper， deep learning networks with better classification and recognition effect without manual extraction of feature information are considered. Considering that the traditional deep learning networks cannot measure the uncertainty in the prediction results well due to the use of point estimation method， this paper proposes a jamming identification method based on Bayesian deep learning. Firstly， the problem of network overfitting caused by uncertain random data is solved by using probabilistic modeling instead of point estimation of network parameter model. Secondly， the LSTM layer is designed to effectively utilize the timing characteristics of radar echo signals， and solve the problem of gradient disappearance during training. The network training and testing are completed based on the measured data of LFM radar active jamming. The experimental results show that the Bayesian method can accelerate the convergence speed of the network and effectively improve the recognition accuracy.