Multi-view learning can effectively exploit the consistency and complementarity among multiple data sources and has become a major research direction in semi-supervised classification. However, the existing methods commonly suffer from several limitations, including the loss of view-specific information caused by premature feature fusion, interference from redundant inter-view noise, and the limited discriminative capability of consensus representation. These issues severely restrict classification performance under low-label settings. To address these limitations, this paper proposes Dual-branch Multi-view Learning with Dual-contrastive Information Bottleneck. The proposed framework constructs a decoupled dual-branch graph convolutional architecture to explicitly separate view-specific representations from cross-view consensus representation, thereby alleviating feature homogenization at the structural level. Furthermore, we design a dual-contrastive information bottleneck optimization mechanism, where the CLUB constraint minimizes redundant mutual information across views to suppress noise, while the InfoNCE constraint maximizes the mutual information between consensus representation and labels to enhance discriminative capability. Additionally, we employ an adaptive attention fusion module to dynamically integrate the dual-branch representations, further refining task-relevant features. The experiments conducted on nine public datasets demonstrate that the proposed method achieves favorable performance improvements over most of the selected comparison methods in semi-supervised classification tasks.