The invention discloses a data-driven iterative image online annotation method. The method comprises the following steps: 1, data preparation and recovery: making a user feed samples to be annotated into a sample buffer pool successively, performing scale detection on the sample buffer pool, performing feature extraction, and feeding the samples to be annotated into a circular annotation module; and 2, circular annotation: at the start of each round of circulation process, calculating a similarity matrix of features through an online similarity measurement model, optimizing the similarity matrix through constraint propagation, performing data clustering on the similarity matrix by a spectral clustering algorithm, selecting a significant category, pushing the significant category to the user for annotation, cycling constantly till remaining samples are not enough to continue annotation, and exiting the circulation process to obtain a personalized label of an input image region given by the user. Through the technology of the method, rapid and accurate annotation of images can be realized; the annotation efficiency is increased; and the method can adapt to a personalized annotation system of the user.