The improved method of random perturbance amplitude section is proposed to increase the convergence speed of stochastic parallel gradient descent (SPGD) algorithm. The SPGD algorithm convergence rate, which can be effected by the random disturbance amplitude, is analyzed when the gain coefficient is fixed. The segmentation random perturbance amplitude method is put forward. The adaptive optics system without wavefront sensor is built with a 61-element deformation mirror to correct the wavefront aberrations, which is simulated by the 65-order Zernike polynomials and the aberrations meet the Kolmogorov spectrum. Compared with the best fixed initial perturbance amplitude SPGD algorithm, the convergence speed increases 1.6 times by adopting the SPGD algorithm based on the segmentation random perturbance amplitude. The improved algorithm is verified to be feasible.