The invention discloses an area-of-interest detection method based on full convolution neural network and low-rank sparse decomposition. The method comprises steps of 1) carrying out super-pixel clustering on an original image, extracting color, texture and edge characteristics of each super-pixel and forming a characteristic matrix based on the characteristics; 2) in an MSRA database, based on agradient descent method, learning to obtain a characteristic conversion matrix; 3) in the MSRA database, by use of the full convolution neural network, learning to obtain high-level semantic prior knowledge; 4) by use of the characteristic conversion matrix and the high-level semantic prior knowledge, converting the characteristic matrix; and 5) by use of the robust principal component analysis method, carrying out low-rank sparse decomposition on the converted matrix, and according to sparse noise obtained through the decomposition, calculating a saliency map. According to the invention, themethod is used in an image preprocessing process, and can be widely applied in visual working field like visual tracking, image classification, image segmentation and target re-positioning.