Aiming at high nonlinearity， low computational accuracy or hard convergence of Lagrangian multiplier calculation in the probability density function estimation by the classic maximum entropy method， a new method combining the maximum likelihood estimation (MLE) maximum entropy probability density method with the sequential updating method is proposed in this paper. Lagrangian optimization function with low nonlinearity is established on the basis of MLE. Furthermore， the sequential updating method is proposed which is constrained by the sample origin moments. Because of unsteady in the process of optimization， the transformation formula of Lagrangian multiplier is deduced again to avoid singularity phenomenon caused by matrix inversion. By analyzing several common distribution and reliability issues using the MLE maximum entropy probability density method and the classic maximum entropy probability density method， it is found that the MLE maximum entropy probability density method has advantage of low nonlinearity and simple form in the optimization function， while the new combination method does well in computational accuracy and optimization convergence.