Aiming at the problem that pilot overhead required by downlink channel estimation of FDD (Frequency-Division Duplexing) massive MIMO (Multiple-Input Multiple-Output) was unaffordable, a pseudo-random pilot optimization scheme based on Compressive Sensing (CS) techniques with non-orthogonal pilot at the base station and the objective to minimize the cross correlation of the measurement matrix was proposed firstly. Then, a crossover and mutation judgment mechanism and an inner loop and outer loop mechanism were introduced to ensure the optimization of pilot sequence. Secondly, a Channel State Information (CSI) estimation algorithm based on CS techniques by utilizing the spatially common sparsity and temporal correlation in wireless MIMO channels was presented. Matrix estimation is performed by using LMMSE (Linear Minimum Mean Square Error) algorithm to accurately obtain CSI. Analysis and simulation results show that compared with random search pilot optimization scheme, location-based optimization scheme, local common support algorithm, Adaptive Structured Subspace Pursuit (ASSP) algorithm, Orthogonal Matching Pursuit (OMP) algorithm and Stepwise Orthogonal Matching Pursuit (StOMP) algorithm, the proposed algorithm can significantly achieve good channel estimation performance in the case of low pilot overhead ratio and low Signal-to-Noise Ratio (SNR).