Objective: The random forest algorithm was used to construct a rapid screening diagnostic prediction model for children with autism spectrum disorder, to provide the references for early detection, early diagnosis of ASD children, and to reduce the pressure of ASD clinical diagnosis and assessment. Methods: The random forest algorithm of machine learning was applied to build the auxiliary diagnosis model. Totally 346 ASD children and 90 normal children were evaluated by Social Responsiveness Scale and Vineland Adaptive Behavior Scales. ROC curve, and accuracy was used to evaluate the models. Results: Among the models, the accuracy of 13 feature factors and 7 feature factors were above 0.9, the sensitivity was up to 0.927, the specificity was up to 0.936 and the AUC was up to 0.979. The accuracy, sensitivity, specificity and AUC of the model were 0.943,0.959,0.931 and 0.978 respectively. The fitting and generalization effects of the three models were all satisfactory. Conclusion A random forest model based on the SRS Scales and Vineland Adaptive Behavior Scales can be used to diagnose ASD accurately and provide scientific basis for the development of rapid screening and diagnosis tools.