Objective To analyze the factors affecting pathological complete response (pCR) of triple-negative breast cancer (TNBC) patients after neoadjuvant chemotherapy, and construct a nomogram to forecast the pCR rate. Methods The clinical and pathological data of 348 TNBC patients who received neoadjuvant chemotherapy in the Air Force Medical University-Affiliated Xijing Hospital from May 2018 to May 2021 were collected and set as modeling set. The clinical and pathological data of 69 TNBC patients who received neoadjuvant chemotherapy in the Xi'an No.3 Hospital from May 2018 to May 2021 were collected and set as validation set. The clinical and pathological characteristics were compared between the modeling set and the validation set. In the modeling set, the independent risk factors of pCR in TNBC patients after neoadjuvant chemotherapy were screened by LASSO regression model analysis, and the nomogram model was constructed. Internal validation of the model was conducted using Bootstrap method, and the discrimination of the model was assessed by receiver operating characteristic (ROC) curve. The accuracy of the model was evaluated by the calibration curve and the clinical benefits and application value of the model were evaluated by clinical decision curve analysis (DCA). Results There were significant differences in surgical method and T stage between the patients in modeling set and validation set (P<0.05). The results of analysis of LASSO regression model showed that T stage, N stage, the use of platinum drugs and clinical efficacy evaluation were independent risk factors of pCR in TNBC patients after neoadjuvant chemotherapy (P<0.05). Based on the above variables, the nomogram models were constructed. In modeling set, area under curve (AUC) was 0.811 (95%CI 0.763-0.859); in validation set, AUC was 0.801 (95%CI 0.727-0.928). The Bootstrap method showed the C-index for internal validation was 0.79, indicating the model has good discrimination in both the modeling and validation sets. The calibration curve analysis showed that model predicted pCR rates had a good consistency with the actual observed values, and the DCA showed that model can bring clinical benefit. Conclusion The nomogram can accurately predict the pCR rates of TNBC patients after neoadjuvant chemotherapy and provide scientific basis for clinical diagnosis and treatment.