Active learning is one of the focuses in the field of machine learning, aiming to solve the unlabeled problem of samples. In this paper, a three-way decision model is applied to active learning. By introducing decision functions, the unlabeled samples are divided into three different parts: positive region, boundary region and negative region based on the uncertainty of unlabeled samples. Different solutions are adopted to process samples for each region. Then, an active learning method based on the three-way decision model, namely TWD{_