MIMO radar imaging relies on data acquired by a large number of transceivers, and a small number of MIMO radar can be used to realize high resolution imaging of the target by means of compressed sensing sparse recovery theory. This paper utilizes the Patterncoupled sparse bayesian learning algorithm applied to MIMO radar imaging. For this reason, the paper proposes a MIMO radar imaging method based on PCSBL. The method utilizes the corresponding characteristics at the target adjacent scattering points described by a patterncoupled hierarchical Gaussian prior and the Expectation Maximization algorithm for realizing the iterative estimation of the hypeparameter, and for further reconstructing the radar target block region accurately. The simulation results show that this method is better than the traditional Fourier and Sparse bayesian algorithm, and is better than the OMP algorithm in scattering point reconstruction.