The direction-of-arrival （DOA） estimation is the premise of beamforming for hybrid massive multiple-input multiple-output （mMIMO） systems. The subspace methods based on covariance matrix reconstruction suffer from a large performance loss under the conditions of correlated signals and limited snapshots. To address the above challenges， this paper proposes a DOA estimation method for hybrid mMIMO systems via sparse Bayesian learning （SBL）. It can be seen that the problem of DOA estimation for hybrid mMIMO systems is transformed into the issue of sparse signal recovery， bypassing the spatial covariance matrix reconstruction and avoiding the performance loss caused by the subspace methods. By using variational Bayesian inference （VBI）， unknown parameters are estimated adaptively， which significantly improves the robustness of noise and correlated signals and enhances the performance of DOA estimation in the case of limited snapshots. Numerical simulation results verify the superiority of the proposed method.