Abstract Learning the disentangled representation of interpretable generative factors of data is one of the foundations to allow artifificial intelligence to think like people. In this paper, we propose the analogical training strategy for the unsupervised disentangled representation learning in generative models. The analogy is one of the typical cognitive processes, and our proposed strategy is based on the observation that sample pairs in which one is different from the other in one specifific generative factor show the same analogical relation. Thus, the generator is trained to generate sample pairs from which a designed classififier can identify the underlying analogical relation. In addition, we propose a disentanglement metric called the subspace score, which is inspired by subspace learning methods and does not require supervised information. Experiments show that our proposed training strategy allows the generative models to fifind the disentangled factors, and that our methods can give competitive performances as compared with the state-of-the-art methods