This paper is concerned with the problem of H∞ model reduction for Takagi–Sugeno (T–S) fuzzy stochastic systems. For a given mean-square stable T–S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an H∞ performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods. Refereed/Peer-reviewed