To address issues such as poor output consistency, information loss, and blurred boundaries caused by incomplete truth labeling in current weakly-supervised point cloud semantic segmentation methods, a weakly-supervised point cloud semantic segmentation method with input consistency constraint and feature enhancement is proposed. Additional constraint is provided on the input point cloud to learn the input consistency of the augmented point cloud data, in order to better understand the essential features of the data and improve the generalization ability of the model. An adaptive enhancement mechanism is introduced in the point feature extractor to enhance the model's perceptual ability, and utilizing sub scene boundary contrastive optimization to further improve the segmentation accuracy of the boundary region. By utilizing query operations in point feature query network, sparse training signals are fully utilized, and a channel attention mechanism module is constructed to enhance the representation ability of important features by strengthening channel dependencies, resulting in more effective prediction of point cloud semantic labels. Experimental results show that the proposed method achieves good segmentation performance on three public point cloud datasets of S3DIS, Semantic3D, and Toronto3D, with a mean intersection over union of 66.4%, 77.9%, and 80.5%, respectively, using 1.0% truth labels for training.