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Individual identification of radio signals is mainly to extract the stray components of radio signals, and achieve the effect of individual identification by analyzing the stray components. Aiming at the non-linear and non-stationary characteristics of the spurious components of line signals, this paper combines the empirical wavelet transform (EWT) with the signal component analysis, and proposes a new signal feature extraction method. Firstly, the EWT is used to decompose the signal adaptively, and the eigenvalue spectrum is analyzed by selecting some signal components which can represent individual differences. Then the fingerprint feature is extracted based on the difference of normalized eigenvalue spectrum of signal components, and then the signal is identified according to the fingerprint feature. The simulation results show that the proposed method has better recognition performance than HHT and local integral bispectrum analysis method, and has better characteristic stability, and is less affected by signal-to-noise ratio.