This study proposes a novel identification method for OFDM emitters to address the issue of low classification accuracy in traditional methods for specific emitter identification, where subtle fingerprint features of OFDM emitters are affected by data signal components and channel noise. Considering the subcarrier spectrum of the short preamble, this method utilizes the fixed frequency boundary-based empirical wavelet transform (FFB-EWT) and a deep residual network. Initially, the short preamble of OFDM signals is extracted to define fixed boundary conditions based on the frequency intervals of the subcarriers in the short preamble. The boundary values in the frequency domain are then applied to FFB-EWT for signal decomposition to remove the subcarrier components containing preamble information. Subsequently, the signal-to-noise ratio of fingerprint features is enhanced by accumulating the null subcarrier components of adjacent frames. Next, a dual-channel residual network called ResNet18, integrated with a non-local attention module and a channel attention module, is used for feature extraction from IQ data inputs, with classification performed via the Softmax function. Finally, the Oracle public dataset is chosen to validate the feasibility of the method. Experimental results demonstrate that the FFB-EWT method achieves accuracy rates of 98.17% and 89.33% for identifying six different emitters under 6 dB and 0 dB conditions, respectively, proving the effectiveness of the method in environments with low signal-to-noise ratios.