The idea of hierarchical processing of convolution features in neural networks has a significant effect on saliency object detection. However, when integrating hierarchical features, it is still an open problem how to obtain rich global information, as well as effectively integrate the global information and of the higher-level feature space and low-level detail information. Therefore, a saliency detection algorithm based on a multi-level global information propagation model was proposed. In order to extract rich multi-scale global information, a Multi-scale Global Feature Aggregation Module (MGFAM) was introduced to the higher-level, and feature fusion operation was performed to the global information extracted from multiple levels. In addition, in order to obtain the global information of the high-level feature space and the rich low-level detail information at the same time, the extracted discriminative high-level global semantic information was fused with the lower-level features by means of feature propagation. These operations were able to extract the high-level global semantic information to the greatest extent, and avoid the loss of this information when it was gradually propagated to the lower-level. Experimental results on four datasets including ECSSD,PASCAL-S,SOD,HKU-IS show that compared with the advanced NLDF (Non-Local Deep Features for salient object detection) model, the proposed algorithm has the F-measure (F) value increased by 0.028、0.05、0.035 and 0.013 respectively, the Mean Absolute Error (MAE) decreased by 0.023、0.03、0.023 and 0.007 respectively, and the proposed algorithm was superior to several classical image saliency detection methods in terms of precision, recall, F-measure and MAE.