In view of the difficulties in model regularization and application universality of existing methods (scaling law evaluation and wave optical simulation evaluation) for evaluating laser atmospheric transport effect,a method based on random forest for evaluating laser atmospheric transport effect is proposed.This method firstly takes random sampling of atmospheric environmental data (temperature,wind speed and turbulence intensity(C2n),etc.) in Yantai and laser parameters (transmission distance,laser power,etc.) as the input data,and multiple phase screen model simulation of turbine power (PIB) as the output data,then with the use of random forests for training and prediction.The results show that the random forest can better represent the multiple regression relationship between input and output than the support vector,and the prediction root mean square error is less than 0.021%.Transmission distance and turbulence intensity C2n have the strongest correlation with PIB,and have the greatest influence on PIB.This method can provide a more perfect theoretical basis for the application of machine learning in the evaluation of laser atmospheric transport effect,and has a certain application value.