A new saliency detection optimization method is proposed to satisfy the accuracy requirement of saliency detection in the natural scene. The method can divide an image into multiple superpixel areas using the simple linear iterative clustering algorithm, and extract the contrast feature of color regions. The general target geometric center is located by the Harris corner detection algorithm. The center probability is used to describe the target space distribution feature, and the adaptive feature fusion for the target location is carried out. Optimization of a saliency map with background suppression and target enhancement is realized based on target space distribution feature and image gray centroid. The continuity of the saliency map can be enhanced by the space smoothing technique for the saliency value. Experimental results show that the test with this method does not only have high precision rate and recall rate, but also has low mean absolute error in several testing sets, and the method can be applied to the saliency detection in complex natural scenes.