Since the extracted image features are interfered by background informa tion,the retrieval accuracy is affected by the failure to extract the required image information.In order to solve this problem,this paper proposes an image saliency region retrieval algorithm based on improved visual geometry group network (VGGnet) and ant colony algo rithm.Firstly, class activation mapping (CMA) algorithm is used to extract the salient region of the image and remove the background information.Then the trained RS-VGG16model is used to extract the salient regional features of the image to represent the image.Principal component analysis (PCA) algorithm is introduced to reduce the dimensionality of high-dimensional feat u res while reducing the loss of feature information.Finally,the ant colony algo rithm was introduced to optimize the retrieval results.In the corel_5000data s et,the image global feature retrieval algorithm based on VGG16network and the traditional bag of features (BOF) image retrieval algorithm are selected for comparative experimen t.Compared with the image retrieval algorithm based on VGG16network,the MAP v alue of the proposed algorithm is improved by about 4.36% on average,and compar ed with the traditional BOF algorithm,the mean average precision (MAP) value is improved by about 16.99% on average.Experimental results show that the algorithm proposed in this paper can remove the interference of image background information well and has better retrieval performance.