Methods based on random forest and gradient boosting regression tree are proposed in order to improve the problem of subjective experience and high cost of calculation time in the effectiveness evaluation of anti-ship missile.By constructing a 3-layer and 19-component anti-ship missile effectiveness evaluation index system,240 operational parameter data samples of anti-ship missile in different states are set up by the improved ADC evaluation model,then the data set is segmented and normalized,combining with cross validation and grid search,two ideal decision tree integration effectiveness evaluation models are obtained.In the simulation test,the accuracy of the two models is high,which proves the practicability of the method and provides new ideas for anti-ship missile effectiveness evaluation.