Abstract: Objective: To construct a deep radiomics model based on MRI CE-T1 and T2WI sequences for predicting cervical lymph node metastasis in oropharyngeal carcinoma. Methods: This study included 123 patients with oropharyngeal carcinoma who were surgically diagnosed at the Department of Head and Neck Surgery, Hunan Cancer Hospital, and the Department of Otorhinolaryngology, Xiangya Hospital of Central South University from July 2019 to October 2025. The patients were randomly divided into a training set and a test set in a 7:3 ratio. Pyradiomics and ResNet101 algorithms were used to extract radiomics features and deep learning features from the CE-T1 and T2WI sequences of all patients. Valuable features were screened using the Spearman correlation coefficient and the least absolute shrinkage and selection operator (LASSO) to construct a radiomics (Rad) model and a deep learning radiomics (DLR) model. The effectiveness of the models was evaluated using the area under the curve (AUC) and decision curve analysis (DCA). The DeLong test was used to compare the significance of differences in the AUC curves between the Rad and DLR models. Results: The DLR model demonstrated the best performance in both the training and test sets, with an AUC of 0.993 (95% CI, 0.982-1.000) in the training set and 0.934 (95% CI, 0.849-1.000) in the test set. Conclusion: The DLR model based on CE-T1 and T2WI sequences shows good performance in preoperatively predicting lymph node metastasis in oropharyngeal carcinoma. It can assist clinicians in formulating more precise and individualized treatment strategies.