In the saliency detection algorithm, there are some problems in the detection of the manifold ranking, such as the over ideal of the background and the incomplete target detection. Aiming at these problems, this study incorporated background identification, BING feature estimation, and weight adjustment in traditional manifold rank algorithm, and a method was proposed based on background awareness. Firstly, through the adaptive color clustering of the boundary area and calculating the synthetic difference degree to get the real background seed point, the real background areas were sensed. Then the BING feature of the image was calculated and the saliency map information was combined to obtain the target position, so as to obtain the complete foreground seed point area. Next, by reconstructing the graph model of the foreground region and using the weighted k-shell decomposition method, we adjusted the connection weight between the nodes in the foreground region to obtain a clear target boundary. The experimental results show that the proposed algorithm is superior to other algorithms in terms of precision, recall, F-measure, and average MAE compared with some classical algorithms.