This paper was concerned with simulation-based inference in generalized models of stochastic volatility with long memory. A more efficient Markov Chain Monte Carlo sampling method was exploited to the analysis of the model, compared with the single step Gibbs sampling method. Based on the truncated likelihood method, in which the long memory stochastic volatility model was expressed as a linear state space model, we utilized the forward filtering backward sampling method to sample all the unobserved volatilities simultaneously. A simulation method for Bayesian prediction analysis of the volatilities was also developed. The simulation study has given the results of estimated parameters and evaluated the performance of our method. Moreover, the prediction analysis of the volatility can be used to control the risk of financial series.