Summary: A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of the slow convergence of traditional fuzzy optimization neural network models. In this new model, the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training, which changes the weights adjusting equations of the network and increases the training speed. Moreover, to avoid the result yielding to local minimum, the transfer function is also revised to sigmoid functions. A case study is utilized to validate this new model, and the results reveal that the new model has fast training speed and better forecasting capability.