To solve the problem that the unlabeled data in the text classification task cannot be immediately marked and the cost is too high， this paper proposes an active learning method for uncertainty based on text classification. The MSDL （Measure sample density by LDA） algorithm is proposed to calculate the unlabeled sample density， and the new metric sample aggregation situation is introduced. The initial training set sample is selected in the densely sampled region， thus making the initial The training set is more representative. The more uncertain samples from the unlabeled samples are added to the training set， the samples are weighted based on the information entropy， and the classifier model is iteratively updated until the expected termination condition is reached. Experimental results show that this method is better than other traditional active learning algorithms in text classification tasks.