The invention relates to the technical field of optimization of the wavelet neural network, in particular to a wavelet neural network weight initialization method based on Bayes estimation, and the state estimation and search idea is adopted in the wavelet neural network weight initialization method. The wavelet neural network weight initialization method based on Bayes estimation comprises the steps of building a wavelet neural network model, unitizing weights, inputting and optimizing wavelet nerve cell weights, and optimizing weights of nerve cells of an output layer. Wavelet neural network weight parameters are linked with the network structure, wavelet types, input data and output target values, the state estimation idea and theory are introduced into initial setting of the weight parameters, wavelet network learning and training capacity is enhanced, the wavelet network has certain pertinence in the initialization phase, and therefore the adaptability of the weights in follow-up network learning and training is improved. Compared with a traditional weight initialization method, the learning efficiency can be effectively improved, oscillation amplitude of network output can be reduced, the rate of algorithm convergence is improved, and network output divergence caused by improper weights can be avoided.