Objective To analyze the risk factors for postoperative pulmonary complications (PPC) in patients undergoing cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) and to construct a prediction model. Methods Collect perioperative information of 298 patients undergoing CRS+HIPEC [including gender, age, American Society of Anesthesiologists (ASA) classification, operation duration, total intraoperative infusion volume, total intraoperative output volume, blood loss, urine volume, colloid infusion volume, crystalloid infusion volume, autologous blood transfusion volume, red blood cell transfusion volume, plasma transfusion volume, and stroke volume variation (SVV) value referred to during goal‑directed fluid therapy (GDFT) during the perioperative period]. According to the presence or absence of PPC after surgery, patients were divided into PPC group (106 cases) and non‑PPC group (192 cases). Stepwise regression analysis was used to screen the characteristic variables of PPC and establish a random forest prediction model. The out‑of‑bag error rate of the random forest prediction model was calculated, and the confusion matrix and parameters (including accuracy, Kappa value, sensitivity, specificity, precision, recall, F1‑Score) were calculated on the training set and test set, respectively. Receiver operating characteristic (ROC) curves were plotted [and the area under the curve (AUC) and 95% confidence interval (CI) were calculated], calibration curves were drawn, and the ranking of independent variables and partial dependence plots of each characteristic variable were plotted. Results Compared with the non‑PPC group, the PPC group had a longer operation duration (P<0.05), increased total intraoperative infusion volume, total intraoperative output volume, blood loss, colloid infusion volume, urine volume, and red blood cell transfusion volume (all P<0.05), and a decreased SVV value during the perioperative period, with statistically significant differences (P<0.05). The stepwise regression analysis showed that operation time, blood loss, red blood cell transfusion volume, and GDFT used the SVV value as the characteristic variable of PPC (P<0.05). The out‑of‑bag error rate of the random forest prediction model was 1.400%. The accuracy of the training set was 1.000, while the accuracy of the test set was 0.952, indicating high overall prediction accuracy of the model. The Kappa value of the training set was 1.000, and the Kappa value of the test set was 0.894, indicating high consistency in the overall prediction ability of the model. The sensitivity and specificity of the training set were both 1.000, while the sensitivity and specificity of the test set were 0.871 and 1.000, respectively, indicating good overall discrimination of the model. The degree of accuracy, recall rate, and F1‑Score of the training set were all 1.000, while the degree of accuracy, recall rate, and F1‑Score of the test set were 1.000, 0.871, and 0.931, respectively, indicating a high predictive ability of the model for positive results. The AUC of the ROC curve for the training set was 1.000 (95%CI 1.000, 1.000), and the AUC for the test set was 0.997 (95%CI 0.962, 1.000), indicating good discrimination ability of the prediction model. From the plot of the sorted independent variables, we can observe the contribution degree of the feature variables to PPC: the order of the impact of feature variables on PPC when SVV value used as a reference is GDFT>operation time>blood loss>red blood cell transfusion volume. The partial dependence plot can show the impact of each feature variable on PPC and the trend of PPC with the change of the feature variables: PPC fluctuates and rises with the increase of operation time; when the blood loss is less than 1000 ml, the fluctuation of PPC changes and the rise is not obvious, but when the intraoperative blood loss is greater than 1 000 ml, the probability of PPC rises significantly; when the red blood cell transfusion volume is greater than 1 000 ml, PPC rises significantly; there is a negative correlation between the SVV value used as a reference for GDFT value and PPC changes. Conclusions The characteristic variables affecting PPC include operation time, blood loss, and transfusion volume of suspended red blood cells when SVV value was used as a reference for GDFT. The constructed random forest prediction model has good discrimination, and accuracy and can be effectively applied to predicting PPC in patients undergoing CRS+HIPEC.