Objective: To investigate the value of radiomics features based on spectral CT iodine map for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid carcinoma. Methods: Seventy-eight patients with papillary thyroid carcinoma who underwent cervical energy spectrum CT within two weeks before surgery were retrospectively analyzed. We included 117 metastatic, 176 non-metastatic lymph nodes, which were then randomly divided into a training set and a validation set in a 3:1 ratio. Radiomics features were extracted and screened from venous phase iodine maps. Logistic regression modeling was used to construct diagnostic models based on CT image features, radiomics signature, and a combination of the two, respectively; a nomogram was then drawn to visualize the combined model. The diagnostic performance, calibration ability and clinical practicability of each model were evaluated by ROC curve, calibration curve and decision curve analysis, respectively. Results: The combined model showed optimal diagnostic performance in both the training and validation sets, followed by radiomics model. These two models outperformed the CT image features model in both the training and validation sets (p<0.05). All models showed good calibration, and decision curve analysis demonstrated the superiority of the nomogram over the other two models in terms of clinical usefulness. Conclusion: The radiomics signature of spectral CT showed good performance in diagnosing lymph node metastasis of papillary thyroid carcinoma. The diagnostic performance was further improved when combined with CT image features, which can be a useful tool Ito assist in clinical decision-making.