Aims: To identify metabolism-associated genes (MAGs) that serve as biomarkers to predict prognosis associated with recurrence-free survival (RFS) for stage I cervical cancer (CC). Patients & methods: By analyzing the Gene Expression Omnibus (GEO) database for 258 cases of stage I CC via univariate Cox analysis, LASSO and multivariate Cox regression analysis, we unveiled 11 MAGs as a signature that was also validated using Kaplan-Meier and receiver operating characteristic analyses. In addition, a metabolism-related nomogram was developed. Results: High accuracy of this signature for prediction was observed (area under the curve at 1, 3 and 5 years was 0.964, 0.929 and 0.852 for the internal dataset and 0.759, 0.719 and 0.757 for the external dataset). The high-risk score group displayed markedly worse RFS than did the low-risk score group. The indicators performed well in our nomogram. Conclusions: We identified a novel signature as a biomarker for predicting prognosis and a nomogram to facilitate the individual management of stage I CC patients. Lay abstract A high rate of mortality may occur as a result of early cancer recurrence, and this can lead to poor prognosis. We aimed to identify a new hallmark linked to metabolism-associated genes (MAGs) that were associated with recurrence-free survival via comprehensive bioinformatics analysis for use in stage I cervical cancer (CC) patient prognosis. A prognostic model was established and validated based on regression analysis. A high predicted accuracy of the 11 MAGs signature was identified. Additionally, we developed a nomogram. In conclusion, a reliable 11 metabolic gene signature was discovered that may provide a potential biomarker for stage I CC prognosis, and a metabolism-related nomogram was constructed to facilitate the individual management of patients with stage I CC.