OBJECTIVE： To establish antibiotics use rationality evaluation model in type Ⅰ incision surgery patients， and to provide reference for prescription review of clinical pharmacists. METHODS： Totally 432 inpatients underwent type Ⅰ surgical incision in a hospital from Jan. 1st- Dec. 31st， 2017 were selected as the research objects. The information of diagnosis and treatment including age， nosocomial infection， the number of kinds of antibiotics used were extracted. Based on the results of clinical pharmacists’ comments on the antibiotics use rationality in patients’ prevention and treatment， non-conditional Logistic regression and support vector machine （SVM） in machine learning method were used to convert clinical pharmacists’ comments into objective index that can be recognized by the machine learning model， using categories of antibiotics （preventive or therapeutic use） as dependent variables and the patient’s diagnosis and treatment information as independent variables. Classification and identification model was established for antibiotics use rationality in type Ⅰ incision surgery patients. Using sensitivity， specificity and Youden index as indexes， established mode was validated on the other 61 samples of type Ⅰ incision surgery patients. The rationality of antibiotics prescriptions in type Ⅰ incision surgery patients before （by manual review， Jan.-Dec. 2017） and after （Jan.-Oct. 2018） using the model were collected， and the effects of the model were evaluated. RESULTS： The sensitivity， specificity and Youden index of non-conditional Logistic regression model were 65.63％， 75.00％ and 40.63％， respectively. Main parameters of the model established by SVM included gamma 0.01， cost 10， sensitivity 92.19％， specificity 87.50％， Youden index 79.69％. The model established by SVM was better than non-conditional Logistic regression. SVM was used to validate established mode， and sensitivity， specificity and Youden index were 100％， 88.57％ and 88.57％， respectively. Compared with before using the model， the evaluation ratio increased from 69.44％ to 100％， the rate of prophylactic use of antibiotics decreased from 23.84％ to 16.43％， the rate of rational drug type selection increased from 37.86％ to 54.39％， and treatment course shortened from 5.01 days to 3.26 days after using the model. CONCLUSIONS： Established antibiotics use rationality evaluation model in typeⅠincision surgery patients by SVM in machine learning method fully covers all the patients， promotes rational use of antibiotics in typeⅠincision surgery patients， and provides a new idea for pharmacist prescription comment.