Aimed at the problems that recognition rate is low, and stability of anti jamming and anti noise is poor, with the result that individual classification of communication emitter is poor and interference ability of fingerprint feature extraction algorithm of communication emitter is poor, a method based on empirical mode decomposition and singular value decomposition is proposed. The effect of noise on fingerprint feature extraction is overcome with signal being subjected to the Empirical Mode Decomposition, the fingerprint feature extraction of signal source is realized by the HilbertHuang Transform and Singular Value Decomposition in combination with Support Vector Machine (SVM) algorithm to complete individual identification of communication source, thus improving the accuracy of the classification and recognition. The experimental verification of the four types of emitter signals show that the ascension of recognition effect is obvious.