The improvement of imaging efficiency in Fourier single-pixel imaging （FSI） is mainly achieved with the help of optimized reconstruction algorithms and optimized sampling methods. However， with a limited number of samplings， FSI cannot accurately sample critical frequencies， resulting in poor imaging quality. To solve this problem， a strategy for adaptive sampling of spectral features is proposed. First， the degree of concentration of energy in the Fourier domain is investigated as a way to determine the optimal radius of low-frequency equidistant pre-sampling， and further， the corresponding Fourier coefficients are measured by means of pre-sampling the low-frequency components to estimate the key spectral positions， which ultimately realizes the image reconstruction. Compared with the adaptive sampling method based on energy continuity in the high-frequency direction， this method can adaptively select better sampling paths for different spectral feature targets， obtain the key Fourier coefficients， and then improve the imaging quality， with a peak signal-to-noise ratio increase of 2.28 dB and a structural similarity increase of 15.83%. Therefore， this method has the advantage of efficient spatial information acquisition in response to FSI of unknown feature targets， and is expected to be applied in single-pixel fast real-time imaging.