Objective To analyze the influencing factors for catheter-associated infection (CAI) in chemotherapy treated patients after indwelling peripherally inserted central catheter (PICC) based on a random forest model. Methods 400 tumor patients who received chemotherapy and PICC were selected and divided into the training set (n=300) and the test set (n=100) in a 3:1 ratio through computer-generated random number. Patients in the training set were subdivided into the non-infection group and the infection group based on the occurrence of infection. Clinical data from two groups of patients were compared. Influencing factors for the occurrence of CAI after PICC were analyzed with multivariate logistic regression model and the integrated classification algorithm of random forest model, and the predictive performance of the two methods was compared. Results Among 300 chemotherapy treated patients in the training set, 32 cases (10.67%) experienced CAI. Compared with the non-infection group, patients in the infection group had more single punctures for catheterization, longer PICC retention time, larger proportion of catheter movement, larger proportion of complication with diabetes, higher frequency of dressing changes, lower white blood cell count and immune function (all P<0.05). PICC retention time, catheter movement, complication with diabetes, dressing change frequency, white blood cell (WBC) and immune function were independent influencing factors for CAI after PICC (all P<0.05). The random forest model showed that ranking by the importance of different influencing factors was as following: PICC retention time, catheter movement, complication with diabetes, WBC, dressing change frequency and immune function. The integrated classification algorithm of random forest model for predicting the occurrence of CAI in chemotherapy treated patients showed that the area under the receiver operating characteristic (ROC) curve (AUC) was 0.872, which had better prediction performance compared with the logistic regression model (AUC=0.791). Conclusion PICC retention time, catheter movement, complicated with diabetes, dressing change frequency, WBC level and immune function are independent influencing factors for CAI in chemotherapy treated patients. The integrated classification algorithm of random forest model can be used to predict CAI in chemotherapy treated patients, and its prediction performance is better than that of the logistic regression model.