Abstract: Aiming at the practical problem that the current point cloud understanding task based on deep learning requires a large amount of labeled data and the labeling of 3D point clouds is extremely time-consuming, we propose an active point cloud labeling method based on contrastive pre-training. The unlabeled samples are actively screened and labeled by alternately running the contrastive learning pre-trained module and the active learning labeling module, thereby improving the model performance under weak supervision. This proposed method first pre-trains the feature extraction module based on the idea of contrastive learning, fixes the parameters of the model, and then uses the model for the active learning module, designs selection indicators based on uncertainty and feature diversity, and calculates the features of unlabeled point cloud data, then selects the desired points to be labelled. The validity of this method is verified in the point clouds understanding tasks. The ModelNet40 dataset is used for verification. The experimental results show that this method can effectively improve the performance of the model under weak supervision. Compared with the randomly selected method, our method can improve the accuracy rate by more than 20%, and finally achieve 73% accuracy under the data labeling, close to 10% on ModelNet. The result on ShapeNet with little data is also promising, which is close to the level of supervised training with the accuracy of 91% under 1000 annotations.