Concerning the problem that the research methods of relation extraction for distant supervision datasets have a lot of label noise, a dynamic label method applied to the hierarchical attention mechanism relation extraction model was proposed. Firstly, a concept of generating dynamic label based on the similarity of relation categories was proposed. Since the same relation labels contain similar feature information, calculating the similarity of relation categories of feature information is helpful to generate the dynamic label corresponding to the feature information. Secondly, the scoring function of the dynamic label was used to evaluate whether the distant supervision label was noise and to determine whether a new label was needed to generate to replace the distant supervision label, and the influence of label noise on the model was suppressed by adjusting the distant supervision label. Finally, according to the dynamic label, the hierarchical attention mechanism was updated to focus on the effective instances, the importance of each effective instance was relearned and key relation feature information was further extracted. The experimental results indicate that, compared with the original hierarchical attention mechanism relation extraction model, the proposed method has the Micro and Macro scores increased by 1.3 percentage points and 1.9 percentage points respectively, realizes the dynamic correction of the noise label, and improves the relation extraction ability of the model.