The pond water temperature is one of the most important parameters which directly affect the feeding, growth, livability and reproduction of aquaculture animals. Thus it is significant to grasp the pond water temperature change for the healthy aquaculture. In order to solve the problems of low precision and poor robustness of traditional forecasting methods, a short-term prediction model of water temperature in aquaculture pond was proposed based on BP neural network optimized by genetic algorithm, and pond aquaculture water temperature prediction system was designed and developed. Firstly, the principal component analysis (PCA) was used to ensure the factors that influenced the water temperature in aquaculture pond. Secondly, the genetic algorithm and BP neural network were integrated to optimize initial weights and threshold. The method not only can get optimal parameter, but also can reduce the errors generated by random initialization. Thirdly, the short-term prediction system was developed by using Java language based on B/S architecture. Finally, the system was applied in Yixing City, Jiangsu Province. Results showed that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) from GA-BP neural network method were 0.1968, 0.0079 and 0.0592, respectively. It was clear that GA-BP neural network was better than BP neural network algorithm. The research result met the practical needs of the pond water temperature management.