Due to the rapid development of deep learning, salient target detection models have achieved significant progress. However, these models ignore the center prior in the collected images, that is, there is a greater probability of being in the center area for the salient object. The hidden information in the image is not fully utilized, which hinders further improvement of the detection result. We propose a center pooling algorithm that realizes the combination of image center prior and deep learning. The algorithm dynamically modifies the size of the receptive field according to the probability of the existence of salient objects and solves the problem of ignoring the importance of different regions when using the same pooling receptive field in the entire image by the standard pooling method. At the same time, the proportion of the foreground in the image is increased, and the problem of category imbalance is alleviated. In addition, a distance-weighted affinity loss function is also proposed to supplement the keyspace information by learning the grouping or separating force between pixel pairs at different distances and increase the confidence of edge regions. To further improve the detection results, a background detection subnetwork is introduced. Through the interactive learning of the foreground and background subnetworks, the opposite relationship between the predicted maps is explored. Finally, experiments on six different related datasets are conducted, and comparisons with other models prove that our model achieves the best performance.