This paper presents a research on modeling and prediction with wavelet neural network in the nonlinear time series of gas emitted. Because accurately predicting the amount of gas emitted from the mine is a very important matter for safety, this paper proposes a new algorithm of wavelet neural network model for time series gas emission prediction. The nervous cells function is the basis of nonlinear wavelets. A wavelet network composed by the wavelet basis function is computed by an expansion and contraction factor and a translation factor to reach the global best approximation effect. Which wavelet basis function has the features of extraction capabilities, self-learning neural network and wavelet transform of the localized nature. The intrinsic defects of artificial BP neural network, e.g., its slow learning speed, difficulty to determine rationally the network structure and existence of partial minimum points, are solved. The simulation results obtained show that the new prediction system has faster convergence and more accurate prediction.