The liver is one of the most ordinary metastatic sites of gastroesophageal junction adenocarcinoma and significantly affects its prognosis. Therefore, this study tried to construct a nomogram that can be applied to predict the likelihood of liver metastases from gastroesophageal junction adenocarcinoma. 3001 eligible patients diagnosed with gastroesophageal junction adenocarcinoma between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were involved in the analysis. Patients were randomly divided into a training cohort and an internal validation cohort using R software, with an allocation ratio of 7:3. According to the consequences of univariate and multivariate logistic regression, we constructed a nomogram for predicting the risk of liver metastases. The discrimination and calibration ability of the nomogram was appraised by the C-index, ROC curve, calibration plots, and decision curve analysis (DCA). We also used Kaplan–Meier survival curves to compare differences in overall survival in patients with gastroesophageal junction adenocarcinoma with and without liver metastases. Liver metastases developed in 281 of 3001 eligible patients. The overall survival of patients with gastroesophageal junction adenocarcinoma with liver metastases before and after propensity score matching (PSM) was obviously lower than that of patients without liver metastases. Six risk factors were finally recognized by multivariate logistic regression, and a nomogram was constructed. The C-index was 0.816 in the training cohort and 0.771 in the validation cohort, demonstrating the good predictive capacity of the nomogram. The ROC curve, calibration curve, and decision curve analysis further demonstrated the good performance of the predictive model. The nomogram can accurately predict the likelihood of liver metastases in gastroesophageal junction adenocarcinoma patients.