Aiming at the problem of poor visual effect and slow processing speed of existing image retargeting methods, a content-aware image retargeting method based on principal component analysis and blocking is proposed. First, the principal component analysis method is used to fuse the gradient map and the saliency map to extract more abundant image features to avoid the distortion of the main information. Then, the adjacent seams are replaced by the mean value to avoid pixel incoherence. Finally, according to the size of the column energy value in the energy map, the image is divided into salient regions and non-salient regions, and the blocks are scaled in parallel to pay more attention to image features and improve operating efficiency. The experimental analysis is carried out on the MIT RetargetMe, DUT-OMRON and NJU2000 datasets, and the subjective perception, the objective factor running time and SIFT-flow are used as evaluation indicators to compare with several commonly used algorithms. The experimental results show that the method ensures the integrity of the image subject information, and the average running time is 1/3 of the seam carving algorithm. The proposed method not only has better visual effect, but also can reduce the computational complexity.