Objective To explore the clinical value of the 3D convolutional neural network (CNN) model in assisting magnetic resonance imaging (MRI) for the diagnosis of multiligament knee injuries (MLKIs). Methods A total of 100 patients with MLKIs who were confirmed by arthroscopy at the Department of Imaging of the Second Affiliated Hospital of Bengbu Medical University from January 2022 to December 2025 were retrospectively enrolled and divided into a training set (70 cases) and a test set (30 cases) at a 7 vs. 3 ratio. Based on the 3D CNN architecture, the 3D U-Net model was used to achieve automatic segmentation of knee joint MRI data and assist in the diagnosis of MLKIs. The diagnostic efficiency of the model, junior physicians, and senior physicians for injuries of the four major ligaments was compared. The impact of model assistance on senior physicians’ image interpretation time, diagnostic consistency, and National Aeronautics and Space Administration-Task Load Index (NASA-TLX) scores was evaluated. Image quality of misclassified cases were also analyzed. Results The model’s sensitivity (92.5%), specificity (89.7%), and accuracy (91.5%) were significantly superior to those of junior physicians (81.3%, 78.2%, 80.0%) (all P＜0.01), and its AUC (0.94) showed no statistically significant difference from that of senior physicians (0.96) (P＞0.05). There was no statistically significant difference in the diagnostic accuracy of the 4 ligaments (χ2＝2.15,P＝0.54). With model assistance, senior physicians’ image interpretation time decreased from (12.5±3.2) min to (7.8±2.1) min, diagnostic consistency improved from 70.8% to 91.7%, and NASA-TLX scores decreased from (42.5±10.2) to (28.3±8.5) (all P＜0.05). False positives were mainly caused by edema signals or partial volume effects (46.67%), while false negatives were mainly due to micro-tears (70.00%). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the misdiagnosed cases were significantly lower than those of the correctly diagnosed cases (all P＜ 0.05). Conclusion The diagnostic efficiency of the 3D U-Net model based on the 3D CNN architecture for MLKIs is comparable to that of senior physicians. It can significantly reduce the image interpretation time, alleviate the cognitive load, and improve the diagnostic consistency.