The algorithm was proposed based on the empirical mode decomposition and regression analysis to extract and identify the characteristic information of spatial heterodyne spectroscopy. The spectrum which was obtained by pre-processing the original probe data was decomposed into several intrinsic mode function components by empirical mode decomposition and the each order IMF′s Pearson correlation coefficient was calculated with the original spectral signal. According to the correlation coefficient classification criteria, the demarcation point of the background and target information reconstruction will be determined. Then the Pearson correlation coefficient between the reconstructed background and the measured background was calculated to determine the empirical mode decomposition results. At the same time, the signal-dominated components were de-noised respectively by the wavelet soft threshold and then the pure target characteristic signal was reconstructed. By using multiple linear regression analysis to process the target characteristic information and the original interference spectral information, the optimal coefficients of time-domain filtering will be obtained. The filter will be constructed to extract the target. Finally, the signal of extracted target will be identified by Pearson correlation coefficients. The experimental results show that the background and the target can be separated by the empirical mode decomposition. In the case of unknown background signal, the empirical mode decomposition and regression analysis can be used to extract the characteristic spectrum of potassium resonance.