Aiming at the recognition of radar jamming emitters, we proposed a method for identifying the individual interference source by using the difference between each emitter's phase noise characteristics. By using information entropy and fractal theory, 39-dimensional features were extracted by multi-domain joint feature extraction method on the basis of time domain, frequency domain, wavelet domain and bispectrum domain. Considering that the direct use of classifiers for processing high-dimensional data is prone to fall into the “curse of dimensionality”, and in order to effectively remove the redundant information and maintain a high recognition rate, we proposed a feature dimension reduction method based on the fusion of ReliefF salgorithm, Fisher criterion and correlation coefficient criterion by use of the Sequential Forward Search (SFS) strategy. The optimal feature subsets of the corresponding original data were obtained. The experimental results show that, compared with the traditional feature selection method, the proposed method can effectively reduce the feature dimension and maintain high classification accuracy.