Background and Aims Pancreatic cancer is a highly aggressive malignancy and patients with pancreatic cancer will face a dismal prognosis. Unlike other cancers, the incidence of pancreatic cancer has continued to increase over the past few years with little improvement in survival rates. The prognostic indicators of the TNM staging system currently used in clinical practice to assess the prognosis of patients are relatively limited. Therefore, this study was designed to construct a dynamic online nomogram for clinical prediction of prognosis of pancreatic cancer patients, so as to provide guidance for clinical individualized treatment.Methods Information of patients with pancreatic cancer from 2000 to 2018 was extracted from the SEER database, and patients were randomly allocated to the training cohort and validation cohort at a ratio of 7∶3. Univariate and multivariate Cox regression analyses were used to identify the prognostic risk factors, and dynamic online nomogram was constructed using R software. The C-index, area under the curve (AUC) of time-dependent ROC curves, calibration plot, and decision curve analysis (DCA) was used to assess the clinical utility of the nomogram. Finally, the pancreatic cancer patients were divided into high-risk and low-risk groups according to the nomogram, and the prognostic results of the two groups of patients were compared by Kaplan-Meier survival curves.Results A total of 12 175 patients with pancreatic cancer were screened. Age, degree of tumor differentiation, primary tumor site, T stage, N stage, M stage, surgery, chemotherapy, and tumor size were independent influencing factors for OS (all P<0.05). In the training cohort, the C-index for the OS nomogram was 0.759 (95% CI=0.745-0.772), and the AUC values for predicting the 1-, 3- and 5-year OS were 0.828, 0.842, and 0.849, respectively. In the validation cohort, the C-index was 0.756 (95% CI=0.735-0.776), and the AUC values for predicting the 1-, 3- and 5-year OS were 0.820, 0.831, and 0.842, respectively. The calibration plot and DCA curves demonstrated good prediction performance of the model in both the training and validation cohorts. Results of Kaplan-Meier survival curves showed that the overall OS of patients in the low-risk group was superior to that of patients in the high-risk group in either the validation cohort or training cohort (both P<0.05).Conclusion The established dynamic online nomogram has a good prediction efficiency and it is helpful for comprehensive prediction of the prognosis of pancreatic cancer patients by a personalized combination of the actual clinical situation of patients. Moreover, the nomogram may have a better clinical application value than the TNM staging system.