Since a single feature is not enough to comprehensively represent the subtle feature differences and thus limit the recognition rate for specific identification of communication emitter， a method of specific identification of communication emitter based on feature fusion is proposed. Firstly， the short-time Fourier transform and bispectrum transform are applied to the original signal to extract time-frequency features and bispectrum features. Secondly， the wavelet fusion technology is integrated to carry out feature fusion. Finally， the residual neural network is used to mine the hidden deep features of the signal to complete classification and recognition. Experimental results show that compared with the single feature method， the recognition effect of the short wave communication signal transmitted by analog signal source after feature fusion has higher recognition accuracy， and it has better recognition effect under the condition of low signal-to-noise ratio（SNR）.