In the salient object detection, the detection results are not ideal when the difference between the background region and the foreground region is not obvious. To address this problem, we propose a saliency object detection algorithm based on neighborhood optimization mechanism. Firstly, the image is segmented by super-pixels. Then, the contrast map and distribution map are established in the CIELab color space and they are merged by a new merging method. Finally, under the constraints such as spatial distance, a neighborhood updating mechanism is established to optimize the initial salient maps. Experimental results show that the algorithm is more effective in salient object detection.