In this paper, a genetic neural fuzzy system (GNFS) is presented and a hybrid learning algorithm divided into two stages is proposed to train GNFS. During first learning stage, Genetic algorithm is used to optimize the structure of GNFS and the membership function of each fuzzy term because of its capability of parallel and global search. On the basis of optimized training stage, the back-propagation algorithm (B-P algorithm) is chosen to update the parameters of GNFS to improve the system precision. The proposed GNFS is used to predict the weight of modeled part in injection process. The process of constructing quality prediction model for injection process based on GNFS is introduced. The results predicted by the constructed model show it can perform very well. The comparison between the presented GNFS and the other model based on regression and the neural network is made. The comparison verifies the proposed GNFS has superior performance and good generalization capability and also can apply to other industrial process.