Objective: To investigate the predictive value of baseline 18F-fluorodeoxyglucose positron emission tomography/computed tomography（18F-FDG PET/CT）derived metabolic parameters in assessing low expression status of human epidermal growth factor receptor 2（HER2）in primary gastric cancer lesions prior to treatment. Methods: We retrospectively analyzed the clinicopathological data and 18F-FDG PET findings from 110 patients with gastric adenocarcinoma who underwent 18F-FDG PET/CT examination before treatment. Among them, 35 exhibited HER2 absent expression, 46 demonstrated HER2 low expression, and 29 showed HER2 high expression. The Kruskal–Wallis H test and chi-square test were used to compare clinical characteristics and PET metabolic parameters among the three groups, followed by post-hoc pairwise comparisons（with Bonferroni correction for the significance level）. LASSO-Logistic regression analysis was adopted to screen the influencing factors predicting HER2 low expression in primary gastric cancer lesions and construct the predictive models. Employ the Bootstrap method（involving 1,000 repeated samplings）for internal validation. Plot the receiver operating characteristic（ROC）curve and compute the area under the curve（AUC）to assess the predictive values of each model. Calibration curves and decision curve analysis（DCA）were adopted to assess the calibration and clinical utility of the models. Results: In terms of clinical characteristics, there were statistically significant differences in the degree of differentiation and carcinoembryonic antigen（CEA）levels between the HER2 low expression group and the HER2 absent expression group（P<0.05）. There were also statistically significant differences in distant metastasis, signet-ring component, and CEA levels between the HER2 low expression group and the HER2 high expression group（P<0.05）. In terms of PET/CT metabolic parameters, except for the minimum standardized uptake value（SUVmin）at the 40% threshold, all other parameters showed statistically significant differences between the HER2 low expression group and the non-HER2 low expression group（P<0.05）. LASSO-Logistic regression analysis revealed that degree of differentiation, CEA level, maximum diameter of primary lesion, 40% and 80% total lesion glycolysis（TLG）were influencing factors for HER2 low expression. Given the potential multicollinearity between TLG 40% and TLG 80%, two LASSO Logistic regression predictive models（Model 1 and Model 2）were separately constructed by incorporating each of the two indicators together with the other three parameters. Model 1 yielded an AUC of 0.668（95% CI: 0.559–0.752, with a sensitivity of 87.0% and a specificity of 42.2%; Model 2 achieved an AUC of 0.723（95% CI: 0.630–0.804）, with a sensitivity of 65.2% and a specificity of 76.6%. Calibration curves and decision curve analysis（DCA）demonstrated that both models exhibited favorable calibration and satisfactory clinical net benefit. In Model 1, TLG40% was an independent influencing factor for HER2 low expression, with an OR of 0.983（95% CI: 0.970–0.996, P= 0.011）and an AUC of 0.387（95% CI: 0.283–0.491）. In Model 2, TLG80% served as an independent influencing factor for HER2 low expression, with an OR of 0.726（95% CI: 0.586–0.898, P=0.003）and an AUC of 0.510（95% CI: 0.399–0.622）. Conclusion: 18F-FDG PET related metabolic parameters can noninvasively predict the HER2 low expression status in gastric cancer, and the combination with clinical parameters achieves optimal predictive performance, which is promising for clinical application.