The invention discloses a rapid area-of-interest detection method based on depth kernelized hashing and mainly aims to solve problems of low detection precision and low efficiency existing in an image area-of-interest detection method in the prior art. The method comprises steps that in a training process, to-be-trained data sets are classified into multiple categories which are inputted to a depth kernelized hashing supervised learning network framework to acquire corresponding hashing codes, fine tuning of network parameters is carried out according to a label information matrix till the optimal learning result is realized; in a test process, inputted test images are pre-processed, and binary codes are acquired through the trained depth kernelized hashing supervised learning network framework; the area-of-interest position of the image is determined according to a decision function and is marked, and area-of-interest detection and identification are accomplished. Through the method, area-of-interest detection analysis performance of the image can be effectively improved, an area-of-interest detection rate and system framework operation efficiency are improved, and the method can be applied to rapid detection and identification of breast image lumps.