Background: Severe bleeding events caused by Primary immune thrombocytopenia (ITP) in children can be life-threatening or lead to long-term complications. This study aimed to establish a risk assessment model for hemorrhage and provide valuable insights for clinical diagnosis and treatment planning in these children. Procedure: This retrospective study reviewed 500 children with ITP, who were categorized into low-bleeding risk and high-bleeding risk groups. Collected data included disease characteristics and laboratory test, compared the differences of variables between groups. Cohort were randomly split at a 7:3 ratio for training set for model development and test set for model validation, employed six type of machine learning algorithms for bleeding risk assessment model construction and evaluated models based on the ROC curve. Finally employed alignment diagram to visually represent the risk assessment model derived from the optimal algorithm. Results: Age, fever, cytomegalovirus infection, neutrophil percentage, erythrocyte, platelet, activated partial thromboplastin time, aspartate aminotransferase, creatine kinase, creatine kinase MB isoenzyme, urea, creatinine and cystatin C were significant difference between low-bleeding risk and high-bleeding risk groups ( p<0.05). Bleeding risk assessment model include platelet, erythrocyte, creatine kinase, urea, age and cytomegalovirus infection variables presented the best performance among the six prediction models (AUC: 0.815) based on binary logistic regression algorithm. Conclusions: A bleeding risk assessment model for children with primary immune thrombocytopenia which variables include platelet, erythrocyte, etc, based on binary logistic regression algorithm is established and demonstrated the best performance among the evaluated machine learning algorithms.