In view of the difficulty in forecasting aquaculture production, a forecasting model of aquaculture production based on Heuristic Johnson Algorithm Optimization and BP Neural Network(BPNN)is proposed in this paper, which is based on the traditional BP Neural Network, with the intention to solve the problems of long time network training and easily being trapped into local optimal solution. It uses Hheuristic Johnson Algorithm to reduce input neuron, and the cut-and-try method to determine the number of hidden layers, thus to construct the Heuristic Johnson Back Propagation Neural Network (HJA-BPNN) Forecasting Model of high precision and high efficiency. The forecasting results of shrimp production in Shandong province, by means of the forecasting model showed that the root of mean square error is smaller than that that by means of traditional BP Neural Network and GM (1,1) forecasting method, and the learning efficiency is improved by comparing with the traditional BP neural network. The study showed that this forecasting model has more advantages in the model construction of a large number of historical data, which can shorten the modeling time and achieve good forecasting results so as to provide a new feasible method for forecasting aquaculture production.