In this paper,Aiming at the pilot design problem in channel estimation of large-scale MIMO systems, an adaptive autocorrelation matrix reduction parameter pilot optimization algorithm based on channel reconstruction error rate minimization is proposed under the framework of compression perception theory. Firstly, the system model and OMP algorithm are introduced。Secondly, Aiming at minimizing the channel reconstruction error rate，the relationship between the expected value of the correlation decision in each iteration of the OMP algorithm and the reconstruction error rate is analyzed. Aiming at the optimal expected value of the correlation decision, the relationship between the channel reconstruction error rate and the correlation of the pilot matrix column under the OMP algorithm is derived, and the two criteria of optimizing the pilot matrix are obtained: the pilot matrix column correlation expectation and the variance minimization.Then the method of optimizing the pilot matrix is studied, and the corresponding adaptive autocorrelation matrix reduction parameter pilot matrix optimization algorithm is proposed. In each iteration, whether the average column correlation degree of the matrix to be optimized is reduced as the judgment condition. Adjust the autocorrelation matrix reduction parameter value to make the parameters close to the theoretical optimization.The simulation results show that the proposed method has better column correlation property and lower channel reconstruction error rate than the pilot matrix obtained by Gaussian matrix, Elad method and low power average column correlation method.