Objective〓To investigate the risk of refracture after osteoporotic vertebral compression fracture (OVCF) surgery，construct a risk prediction model，and identify effective prevention and treatment measures.Methods〓A total of 119 patients with OVCF admitted to Beijing Jishuitan Hospital from August 2021 to June 2022 were selected as the research subjects.According to the occurrence of postoperative refracture，they were divided into a recurrent group and a non-recurrent group.The recurrent group comprised 22 patients (11 males and 11 females) with a mean age of (72.02±5.58)years，while the non-recurrent group consisted of 97 patients (50 males and 47 females) with a mean age of (70.79±6.81)years.Statistical analysis was conducted on the general characteristics of both groups.The Lasso-Logistic regression model was used to screen the independent variables of recurrent fractures after OVCF surgery.The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used to compare the predictive performance of full variable logistic regression，stepwise logistic regression，and Lasso-Logistic regression.The nomogram model was constructed，and the receiver operating characteristic (ROC) and calibration curve were used to analyze the performance of the nomogram model for recurrent fractures after OVCF surgery.Results〓After a follow-up period ranging from 8 to 20 months，with a mean duration of (12.00±2.40)months.Univariate analysis revealed significant differences between the refracture and non-refracture groups in terms of body mass index (BMI)，bone mineral density T-score，tartrate-resistant acid phosphatase 5b(TPACP-5b)，receptor activator of nuclear factor kappa B ligand (RANKL)，osteoprotegerin (OPG)，postoperative anti-osteoporosis treatment，interleukin(IL)-17，history of long-term glucocorticoid use，spinal deformity index (SDI) value，surgical segment Cobb angle，kyphosis angle，and number of operated vertebral bodies (P＜0.05).The Lasso-Logistic regression model analysis identified an optimal lambda.lse value of 0.049，at which point the variables included in the model were bone density，SDI value，IL-17，and postoperative anti-osteoporosis treatment.Validation using BIC and AIC confirmed the good fitting and predictive capabilities of the constructed model.The nomogram model demonstrated an area under the receiver operating characteristic curve(AUC) of 0.865，with a sensitivity of 95.45% and a specificity of 68.04%.Additionally，the calibration curve indicated a close alignment between the model’s predictions and actual outcomes.Conclusion〓The occurrence of postoperative re-fracture after OVCF is affected by various aspects during the perioperative period，including BMD T-value，SDI value，Cobb angle of the operated segment，IL-17，posterior convexity angle，postoperative anti-osteoporosis treatment，and the number of operated vertebral bodies，based on which the risk of re-fracture of the patients can be predicted effectively，providing a reference basis for the prevention and treatment of re-fracture in the clinic.