Objective To analyze and screen independent prognostic factors for early ovarian cancer after systematic lymph node resection, and construct and validate nomogram to predict survival. Methods We collected 3906 early stage ovarian cancer patients with systematic lymph node resection diagnosed at 2010 to 2017 from the SEER database, and obtained relevant clinical information. All cases were randomly divided into a training group (2736 cases) and a validation group (1170 cases) with a 7〖DK〗∶3 ratio. We used univariate and multivariate Cox regression analysis to screen clinical information and identify independent risk factors for prognosis, and a nomogram was constructed based on independent risk factors. Then the consistency and accuracy of the prediction model was evaluated using C-index, area under curve (AUC), and calibration curve. Results The results of univariate and multivariate analysis showed that patients over 75 years old, grade III or IV, higher stage, and mucinous pathology were independent risk factors for prognosis. Age<60 years old and white population were independent protective factors. A nomogram was constructed by using independent prognostic factors, and the C-index values in the training group and validation group were 0.72 (95% CI,0.69-0.74) and 0.71 (95% CI,0.67-0.75), respectively. The AUC for 1, 3, and 5 year prognosis in the training group and validation group were 0.683, 0.738, 0.745, and 0.736, 0.754, and 0.722, respectively. The calibration curves all showed good consistency. Conclusion Based on the data of early stage ovarian cancer patients after lymph node resection in the SEER database, a nomogram perdition model was constructed. The model had good predictive effect and is helpful in clinical survival and prognosis evaluation rapidly