Abstract: Due to the irregular shape and large difference of lung tumors in 3D medical images, the feature extraction of lesions is insufficient and the recognition accurancy is not high, a 3D cross-modal lung tumor recognition model 3D-CConvFormer based on CNN and Transformer is proposed. Firstly, three Nets are utilized to learn the 3D PET, CT and PET/CT medical images. Secondly, a ConvFormer model is designed to fuse global features and shallow local features, and self-correcting convolution is utilized to effectively extend the receptive field to improve the extraction ability of lesion information in each modality. Finally, a dual-branch cross-modal feature interaction block with different resolutions is designed to interactively enhance cross-modal features and capture 3D multimodal detail information, The module can interactively enhance cross-modal features extracting ability using two global attention mechanisms to cross-learn different modal, global-local information. The 3D multimodal dataset of lung tumor is used in the experiments, with a total of 3 173 patients. Under the premise of better parameters and computation time, the accuracy of 89.25% and the AUC value of 88.74% are obtained by the 3D-CConvFormer model, which provided reliable computer-aided diagnosis for three-dimensional multi-mode lung tumor disease.