Abstract: The present disclosure provides a method and device for constructing an autism spectrum disorder (ASD) risk prediction model. The method includes: establishing a first data table and a second data table based on case information of a sample set, obtaining a first grouped table set and a second grouped table set according to a preset characteristic arrangement rule and marker grouping rule, training data based on a random forest machine learning algorithm, and importing test data to obtain a first best characteristic combination and a second characteristic combination; and obtaining a first model based on the first best characteristic combination, stratified sampling of the first data table, and the random forest machine learning algorithm, obtaining a second model based on the second best characteristic combination, stratified sampling of the second data table, and the random forest machine learning algorithm, and performing combination to construct an ASD risk prediction model.