<p>In this paper, we consider the composite optimization problem, where the objective function integrates a continuously differentiable loss function with a nonsmooth regularization term. Moreover, only the function values for the differentiable part of the objective function are available in solving that composite optimization problem. To efficiently solve this composite optimization problem, we propose a preconditioned zeroth-order proximal gradient (ZOPG) method in which the gradients and preconditioners are estimated by finite-difference schemes based on the function values at the same trial points. We establish the global convergence and worst-case complexity for our proposed method. Numerical experiments exhibit the advantage of our developed method.</p>