Abstract: Studies the problem of fuzzy H∞ filter for Ito-type nonlinear stochastic systems. With the fuzzy hyperbolic rule base defined, the stochastic fuzzy hyperbolic model (SFHM) is developed. The superiority of SFHM over Takagi-Sugeno (T-S) fuzzy model in practice is mainly that no identification of preconditional structure and complete parameter space are needed, especially SFHM costs obviously less than T-S fuzzy model in computation when a lot of fuzzy rules are needed. Furthermore, based on SFHM, the H∞ filter is designed to transform the second-order nonlinear Hamilton-Jacobi inequality problem which is difficult to solve in the design of nonlinear stochastic H∞ filter into the problem of linear matrix inequality, and the fuzzy hyperbolic H∞ filter is therefore given by solving the linear matrix inequalities instead. Simulation example is provided to illustrate the effectiveness of the proposed approach.