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Due to the difficulty in establishing wine quality prediction model, the modeling method based on fuzzy recurrent wavelet neural network is developed in this paper. Physical and chemical indicators and taster scoring are used as the input and output of the model. The parameters of network, such as centers and widths of membership layer, translation and dilation factors of wavelet function, self-feedback weight factors, and weights of output layer, are trained online by gradient descent algorithm. In simulation experiments, Mackey-Glass chaotic time series is tested firstly, and then the wine quality data of UCI data set is used to verify the quality prediction model. The results show that prediction model based on fuzzy recurrent wavelet neural network has higher prediction accuracy, compared with traditional feedforward neural network.