Addressing the challenges in extracting subtle fingerprint features of communication emitters and the low recognition rate of single-feature identification, a method for individual identification of communication emitters is proposed. This method combines variational mode decomposition (VMD) with multi-domain feature fusion (MDFF), based on the joint application of minimum Bhattacharyya distance (BDmin) and correlation analysis (CA). First, the VMD method based on BDmin is utilized to decompose each symbol waveform of the communication emitter signal into several intrinsic mode functions (IMFs), including low-frequency IMFs containing data information and high-frequency IMFs containing fingerprint information. Next, the correlation coefficients between each IMF and its symbol waveform signal are calculated, with IMFs exhibiting small correlation coefficients being selected as the subtle feature components of the emitter. Time-domain, frequency-domain, and entropy-based multi-feature parameters are then extracted from these subtle feature components and concatenated into a multi-domain feature vector for feature extraction of the communication emitter’s symbol waveform. Finally, a long short-term memory (LSTM) network is employed to sequentially learn and classify the multi-domain feature vectors of each symbol of the emitter signal, thus achieving individual identification and classification of communication emitters. Experimental validation is conducted using the publicly available Oracle dataset. The results show that, when the signal-to-noise ratio (SNR) is 6 dB, the proposed algorithm achieves a recognition accuracy of 96.7%, representing a 22.1% improvement compared to the average reco gnition accuracy of individual domains.