<p>Multiview multilabel learning with incomplete views and missing labels (MVMLL-IVML) aims to make multilabel prediction by exploring incomplete information from multiview multilabel data. Existing methods learn a unified view weight to fuse view information based on the assumption that the view weights for the same view across all samples are consistent. However, in practical applications, the view weights for different samples may be different, even the difference is significant. For the drawbacks, we propose dual-weight fusion framework (DWFF). First, we employ autoencoders to obtain high-level features (HLF) for each view and introduce contrastive learning to increase the ability of the autoencoder to capture rich-view semantic information. Second, we propose the dual-feature comparison (DFC) strategy to obtain view weights by comparing the original features with the reconstructed features. Third, all features are fused based on the obtained view weights and the missing view indicator matrix. Finally, the fused HLF are fed into the classifier. Extensive experimental results demonstrate the effectiveness of the proposed DWFF.</p>