In this study, we propose a complex prior saliency target detection method to solve the problems associated with the saliency maps. These problems are as follows: the saliency maps generated via the existing saliency target detection method under single prior knowledge exhibit incomplete background suppression, isolated background block interference, and lack of foreground area. First, the superpixel segmentation algorithm was applied to extract the edge superpixels, and the primary background set was constructed. Subsequently, the background set was optimized in accordance with the significance of the boundary and the four corners. Then, we proposed the feature difference method with respect to the characteristics of the background superpixels exhibiting a low gradient. Second, a convex hull, which roughly surrounds the foreground area, was constructed and set as the center position of its centroid. Finally, three prior saliency maps were adaptively weighted to obtain the final saliency map. The proposed method was used to perform experiments on the maps obtained using the MSRA and ESSCD datasets. The obtained results prove that the proposed method can solve the aforementioned problems by fusing three types of prior knowledges. It can simultaneously reduce background suppression and obtain a saliency map with the foreground area integrity of a significant degree.