The existing DOA estimation methods based on Nested Sparse Circular Arrays (NSCA) suffer from high computational complexity and the difficulty in fast selecting of super parameters.To solve the problemsan Off-Grid Sparse Bayesian Learning (OGSBL) method based on the improved NSCA is proposed.The covariance matrix of the received signals of the improved NSCA is vectorizedand an extended observation matrix is constructed.Thenthe under-determined DOA estimation is realized by using the off-grid model and the Sparse Bayesian Learning (SBL) algorithm.The simulation results show that the proposed algorithm reduces computational complexitythe super parameters of the model can be adjusted adaptivelyand the performance of the proposed algorithm is better than that of the DOA estimation algorithms based on the original NSCA and the traditional uniform circular arrays under the conditions of low SNRsmall snapshots and multiple sources.