In order to solve the problem of low real-time and low matching accuracy of interference mode in adaptive interference decision making, this paper presents a SVM based on IGA for interference mode adaptive selection. The penalty parameters and kernel function parameters of SVM were optimized by IGA to enhance the learning ability and generalization ability of the model and improve the real-time and accuracy of interference decision making. IGA-SVM is compared with SVM based on GS method in terms of the accuracy and real-time of interference decision. The simulation results show that, in adaptive interference decision making, the real-time performance and interference mode matching accuracy of IGA-SVM are improved compared to traditional GS-SVM.