Objective: To evaluate the predictive efficacy of spectral CT quantitative parameters for central lymph node metastasis (CLNM) in preoperative clinical node-negative(cN0) papillary thyroid carcinoma (PTC) patients. Methods: Clinical and imaging data of 86 surgical pathologically confirmed PTC patients who underwent preoperative spectral CT imaging, were retrospectively collected. Clinical features included age, sex, preoperative levels of thyroid-stimulating hormone(TSH), thyroglobulin (Tg), thyroglobulin antibody(TgAb), thyroid peroxidase antibody(TPOAb), tumor location, maximum short-axis diameter, and the presence of calcification or cystic changes. Spectral CT quantitative parameters were measured in non-contrast(NC), arterial, and venous phases(VP), including iodine concentration (IC), effective atomic number (Zeff), normalized iodine concentration (NIC), normalized effective atomic number (NZeff), dual-energy index, CT values at 40 keV (40keVHU) and 70 keV (70keVHU), and the spectral curve slope (λHU). Differences in clinical and spectral CT features between the CLNM and non-CLNM groups were compared. Receiver operating characteristic curve analysis was used to assess the predictive performance of clinical features, spectral CT features, and a combined model, with area under the curve (AUC), sensitivity, and specificity calculated. Results: Among clinical indicators, sex, Tg level, and cystic changes showed statistically significant differences between CLNM and non-CLNM groups (all P < 0.05). Among non-contrast spectral CT parameters, 40keVHU and 70keVHU values were significantly lower in CLNM group (all P < 0.05). Among arterial phase(AP) spectral CT parameters, all parameters except arterial NZeff differed significantly between two groups (all P < 0.05). Among venous phase spectral CT parameters, 70keVHU value was significantly lower in CLNM group (P = 0.014). Combined model demonstrated the highest predictive efficacy (AUC = 0.896, sensitivity = 86.7%, specificity = 80.4%), followed by spectral CT quantitative parameter model (AUC = 0.835, sensitivity = 73.3%, specificity = 85.7%), and clinical model (AUC = 0.591, sensitivity = 20.0%, specificity = 98.2%; all P < 0.05). Conclusion: Spectral CT quantitative parameters based on lesion characteristics can improve the preoperative prediction of CLNM in cN0 PTC patients, and aid in treatment plan.