<p>Positive and unlabeled learning (PU learning) addresses classification scenarios where only positive and unlabeled samples are available, the latter comprising both hidden positive and negative instances. While most existing PU methods focus on identifying reliable negative samples, they often underutilize the remaining unlabeled data and operate primarily within a single-view framework, limiting their expressive power. To overcome these limitations, this paper proposes SMVPU, a novel similarity-based multi-view PU learning method that effectively integrates multi-view learning principles. SMVPU first extracts reliable negative samples from the unlabeled set and assigns similarity-weighted values to the remaining unlabeled instances. It then incorporates multi-view representations to enhance feature compatibility and discriminability. By leveraging both consistency and complementarity principles across views, the method constructs a robust PU classifier formulated within a large-margin learning framework. The optimization problem is efficiently solved via the Lagrangian dual method. Extensive experiments demonstrate that SMVPU achieves superior performance compared to existing PU learning methods in terms of classification accuracy and stability.</p>