Background: To identify whether patients with estrogen receptor (ER)-low-positive breast cancer can benefit from endocrine therapy, it is of great significance to guide their treatment. We have developed and validated a nomogram to predict the status of ER-low-positive breast cancer based on clinicopathological characteristics. Methods: A retrospective study was conducted on 450 patients with invasive breast cancer. Among them, 139 (30.89%) patients were ER-negative (ER <1%) and 311 patients (69.11%) were ER-positive (ER >10%), and the two groups of patients were randomly divided into training cohort and validation cohort at a ratio of 7:3. Multivariate logistic regression analysis was performed, after which a nomogram for predicting the status of ER was constructed and validated. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The Youden index was calculated to determine the optimal cut-off value. Finally, the nomogram is used to predict the status of 260 ER-low-positive (1-10%) breast cancer. Results: The final multivariate regression model included nuclear pleomorphism, mitoses, TILs, and necrosis. The nomogram showed good calibration and discrimination in both sets (an AUC of 0.804 and 0.828 for the training and validation cohorts, respectively). According to the Youden index, the optimal cutoff value was 0.59. Status prediction was performed for ER-low-positive breast cancer, of whom 63.08% was predicted as negative and 36.92% was predicted as positive. Those who with negative predicted result had lower levels of ESR1 mRNA expression, did not benefit from endocrine therapy, and had a poorer prognosis. Conclusions: We developed and validated a nomogram to predict the status of ER-low-positive breast cancer based on clinicopathological characteristics. The nomogram will facilitate individualized precision treatment of ER-low-positive breast cancer.