The invention discloses a cross-domain image example level active labeling method. Digital image target detection is one of the basic tasks of computer vision, and generally needs a large number of samples with object frame labels to be used for training a machine learning model. However, in real tasks, a large number of training samples of target tasks cannot be obtained due to sensibility and the like, so that the model performance is low, and the model is difficult to promote. According to the method, the unsupervised source domain which is easy to obtain and rich in knowledge is utilized, and efficient example labeling is automatically selected through an active learning technology, so that finer labeling information is obtained, and the data labeling difficulty is greatly reduced; meanwhile, the obtained supervision information is fully utilized, the performance of the model on the target task is efficiently improved, and the participation cost of the user can be remarkably reduced.