In order to solve the problems of single prior information and incomplete salient object detection in the traditional algorithm,a new salient object detection algorithm based on background prior and center prior is proposed.Firstly,the edge nodes of the image are used as background seeds to manifold ranking,and the rough foreground area is obtained; Then,Harris corner detection and clustering are used to detect the prior significance of the center and capture the significant information of the center; Finally,the final saliency map is obtained by fusing the center saliency on the preliminary saliency map.In this paper,the comprehensive index,precision recall curve,F-measure value and mean absolute error (MAE) value of average absolute error are evaluated experimentally.The experimental results on the open data sets MSRA-10K and ECSSD show that compared with 10 mainstream algorithms,the algorithm in this paper has good performance in different evaluation indicators,and can accurately highlight significant targets andimprove the effect of background suppression.