In order to improve the performance of the spectrum detection， and reliable communication in the spectrum congestion and competition complex electromagnetic environment of satellite system， the spectrum detection is converted to a binary classification problem by employing the support vector machine （SVM） algorithm. Specifically， the feature vector， which is used to characterize the signals， is obtained by removing the central and basis vectors from the energy vector and the SVM model for determining the spectrum status is then constructed. Moreover， the optimal parameter of the Gaussian kernel is determined by adopting the simulated annealing （SA） algorithm. Simulation results show that the proposed scheme can achieve better spectrum detection accuracy and increase the detection robustness as well as improve the system throughput and energy efficiency as compared to the existing single threshold and double-threshold base spectrum sensing schemes. The work conducted in this paper could support the construction and development of future cognitive satellite communications systems.