Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to assist in diagnosis and assessment, which can help improve clinical management and provide new ideas for clinical research. To support the development and testing of deep learning models for cervical spondylosis, we have publicly shared a multi-modal and multi-view imaging dataset of cervical spondylosis, named MMCSD. This dataset comprises MRI and CT images from 250 patients. It includes axial bone and soft tissue window CT scans, sagittal T1-weighted and T2-weighted MRI, as well as axial T2-weighted MRI. Neck pain is one of the most common symptoms of cervical spondylosis. We use the MMCSD to develop a deep learning model for predicting postoperative neck pain in patients with cervical spondylosis, thereby validating its usability. We hope that the MMCSD will contribute to the advancement of neural network models for cervical spondylosis and neck pain, further optimizing clinical diagnostic assessments and treatment decision-making for these conditions.