Objective To investigate the influencing factors and predictive efficacy of biochemical persistence/recurrence （BPR） after radical resection for high-risk localized prostate cancer （HR-LPCa），and to analyze the clinical performance of a nomogram model constructed based on the influencing factors in predicting postoperative BPR.Methods A total of 172 patients with HR-LPCa who underwent radical resection at Baotou City Central Hospital，Inner Mongolia Autonomous Region from January 2016 to January 2023 were selected as research subjects. Clinicopathological characteristics were compared between patients with and without BPR，and binary logistic regression was used to analyze the influencing factors of postoperative BPR in HR-LPCa patients. A nomogram model was constructed based on the results of multivariate analysis. The receiver operator characteristic （ROC） curve was used to evaluate the predictive efficacy of the model，and the calibration curve was used to assess the accuracy and practicality of the model.Results Among the 172 patients，42 developed biochemical persistence，32 had biochemical recurrence，and 4 progressed to castration resistance. There were statistically significant differences between patients with BPR （n=74） and without BPR （n=98） in baseline prostate-specific antigen density （PSAD） （t=6.93，P<0.001），pathological International Society of Urological Pathology （ISUP） grade （χ²=17.31，P<0.001），and surgical margin status （χ²=29.29，P<0.001）. Multivariate analysis showed that，baseline PSAD （OR=0.01，95%CI： 0.00-0.04，P<0.001），pathological ISUP grade （OR=0.27，95%CI： 0.12-0.61，P=0.002），and surgical margin status （OR=0.18，95%CI： 0.08-0.40，P<0.001） were independent influencing factors for BPR after radical resection in HR-LPCa patients. Based on the multivariate analysis results，a nomogram model was constructed to predict the risk of BPR after radical resection in HR-LPCa patients，logit（P）=4.84-5.99×baseline PSAD-1.32×pathological ISUP grade-1.72×surgical margin status. The ROC curve analysis showed that，the area under the curve （AUC） values of baseline PSAD，pathological ISUP grade，surgical margin status，and the nomogram model for predicting BPR after radical resection in patients with HR-LPCa were 0.76，0.66，0.71，and 0.88，respectively. The AUC of the nomogram model was significantly higher than that of each of the other three indicators alone （Z=-3.62，P<0.001； Z=-4.59，P<0.001； Z=-2.36，P<0.001）. The nomogram model exhibited a C-index of 0.878 and excellent goodness-of-fit （Hosmer-Lemeshow χ²=3.51，P=0.752）. Calibration curves demonstrated that the predicted curve of the nomogram model closely approximated the ideal reference line without significant deviation，indicated good predictive accuracy.Conclusions Baseline PSAD，pathological ISUP grade，and surgical margin status are all independent influencing factors for BPR after radical resection in HR-LPCa patients. The nomogram model constructed based on the above clinicopathological indicators shows good predictive performance.