<p>Malignancy prediction of lung nodules on computed tomography (CT) is a task of significant clinical importance. The malignancy of lung nodules on CT is not only reflected in some local details but also closely related to several global attributes. However, previous studies often ignore this vital fact, resulting in a bottleneck of performance improvement. In this paper, a collaborative deep learning framework based on adaptive feature fusion (CDLF-AFF) is proposed to improve the malignancy prediction performance. In the CDLF-AFF method, a multi-view input strategy is designed to drive the pre-trained models to capture the abundant spatial information of nodule CT images. Furthermore, a dual-branch architecture is developed to simultaneously learn the local structure features and long-range dependency relations. To improve the feature fusion efficiency, a feature aggregation module is constructed to adaptively fuse the feature maps produced by two different styles of learning branches. The CDLF-AFF method is evaluated on three different datasets. On the benchmark dataset LIDC-IDRI, it achieved an AUC of 97.25% (95% CI: 96.97%-97.54%), representing improvements of 1.47% and 0.86% over the corresponding unimodal models, ResNet-Model and ViT-Model, respectively. On the clinical dataset CQUCH-LND, it achieved an AUC of 94.09% (95% CI: 93.34%-94.84%), showing improvements of 3.41% and 1.39% over the corresponding ResNet-Model and ViT-Model, respectively. On the competition dataset LUNGx, it achieved an AUC of 79.13% (95% CI: 78.07%-80.19%), surpassing the best-performing algorithm listed on the competition leaderboard by 11.13%. These results demonstrate that the CDLF-AFF can effectively predict the malignancy of lung nodules.</p>