Aiming at the problems that traditional effectiveness evaluation methods can not reflect the evolution, emergence and adaptability of the anti-missile equipment system, a data-driven effectiveness evaluation method of anti-missile equipment system was proposed. Based on the analysis of the characteristics of anti-missile equipment system and the shortage of traditional effectiveness evaluation method. the Bayes optimization algorithm was used to optimize the convolutional neural network hyperparameters, and the efficiency evaluation model ofBayes-CNN(Bayes convolutional neural network) was constructed. The flow and steps of Bayes-CNN system effectiveness evaluation algorithm were studied, and a set of completed efficiency evaluation algorithm was formed. Designed and validated the simulation experiment, input a lot of test data to Bayes-CNN model for training and learning, so as to obtain the simulation prediction of the effectiveness of anti-missile equipment system. The experimental results show that the error between the actual and expected output is very small, and the non-linear fitting effect is great so that it had a high degree of feasibility and reliability.