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In this paper, a new method is proposed for identifying chaotic system based on a Wiener-least squares support vector machine (Wiener-LSSVM) model. The model consists of a linear dynamic subsystem followed by a static nonlinear function, which is represented by LSSVM in this paper. The parameters of the linear dynamic part and those of LSSVM are estimated simultaneously by solving a set of linear equations using the least squares (LS) method. The proposed method incorporates partial structure information into the identification process and does not assume that the parameters of linear dynamic part are known. On the other hand, the LS algorithm is more efficient than gradient-descendent-based algorithms for estimating the parameters of Wiener-LSSVM. Three identification examples are given to validate the effectiveness of the proposed method.