To improve the anti-interference test capability of missiles in complex electromagnetic environments， this paper proposed a Bayesian regularization design method for test samples of multi-sensitive factor anti-suppression interference. First， this method analyzed the typical multi-sensitive factors affecting the anti-suppression interference capability of the seeker and then designed a Bayesian regularization network to solve the test sample design problem of missile anti-interference. The constructed Bayesian regularization network for multi-sensitive factor anti-suppression interference tests could establish a nonlinear mapping relationship between different multi-sensitive anti-interference influencing factors and anti-interference capability， greatly reducing the sample size of traditional all-factor traversal tests. Then， the hyperparameter solving method for the constructed network was studied， thereby realizing the Bayesian regularization design method for test samples. Finally， simulation experiments on typical anti-suppression interference were conducted to verify the performance of the proposed test sample design method. The results indicate that the Bayesian regularization test sample design for multi-sensitive factor anti-suppression interference effectively shortens the time cycle， saves test resources， and provides theoretical support for the anti-interference test evaluation of precision-guided missiles.