To solve the problem of ignoring common information in different domains in traditional active learning image classification, a new multi-domain active learning image classification method was proposed to reduce the labeling effort of image classification. It learned a subspace to represent common features among different images. Considering the common features and domain-specific features, the model loss due to each data instance could be divided into two parts, so that the common information could be queried from the common part. The experimental results show that the new method has some precise increase and has 30% less labeling efforts than the single model method and mixture model method. The results reveal that the new method can be widely used in all kinds of image classification tasks with higher precise and efficiency.