Objective To search for the prognostic factors of young patients with gastric cancer, and construct a prognostic prediction model nomogram, so as to provide a more accurate tool for the individualized prognostic evaluation of patients. Methods The information of 2 673 young gastric cancer patients aged 18-44 years diagnosed from 2004 to 2015 was collected from the Surveillance, Epidemiology, and End Results (SEER) database client SEER*Stat 8.3.8. The patients were randomly divided into training set (1 873 cases) and validation set (800 cases) in a ratio of about 7:3 using R 4.0.3 software. Focusing on the cancer-specific survival (CSS) rate, univariate and multivariate analyses were performed using Fine-Gray competitive risk model in the training set to find the influencing factors of CSS in young gastric cancer patients. According to the influencing factors, the prognosis prediction model was established and the nomogram was drawn. Receiver operating characteristic (ROC) curve and calibration curve were used to verify the prediction effect of the model in the training set and validation set. Results The results of multivariate analysis in the training set showed that tumor grade, T stage, N stage, M stage, primary surgery, regional lymph node surgery and chemoradiotherapy were the independent influencing factors of CSS in young patients with gastric cancer. The cumulative 1-, 3- and 5-year CSS rates of young gastric cancer patients in the training set were 54.56%, 29.70% and 23.96%, respectively. The area under curve (AUC) values of ROC curves of 1-, 3- and 5-year CSS rates of nomogram constructed based on independent prognostic factors were 0.817, 0.864 and 0.887 in the training set, respectively, while those were 0.820, 0.899 and 0.890 in the validation set, respectively. The calibration curves showed that the prediction probabilities of the 1-, 3- and 5-year CSS rates in the training set and validation set were basically consistent with the observed probabilities. Conclusion The Fine-Gray competitive risk model can effectively identify the prognostic factors of young patients with gastric cancer, and the prognostic prediction model can effectively predict the CSS of patients, which can help clinicians to make treatment decisions.