Aiming at the problem that the salient objects detection has low accuracy under complex background, an improved Conditional Random Field (CRF) salient objects detection method is proposed by using Hypercomplex Fourier Transformation (HFT). Firstly, this method builds unoriented graphs and extracts the node features on the image; Then, the HFT is reconstructed to obtain the smooth amplitude spectrum and phase spectrum, and the background suppression weights of unoriented graph nodes are obtained. Thus, the multi-scale Gaussian kernel background suppression graphs are preliminarily determined. Finally, they are inputted into the trained Conditional Random Field, the final significant target area is obtained by enhancing target representation. Experimental results show that the proposed method has obviously higher accuracy than the existing methods and can restrain the complex background and lock the specified target location region accurately at the same time. Experiments verify that salient objects detection has good accuracy and robustness under complex background.