As an important indicator in aquaculture, dissolved oxygen has a very close relationship with the growth of aquatic products. In order to accurately predict dissolved oxygen content in aquaculture ponds and reduce aquaculture risk, a combined fuzzy neural network model for prediction based on wavelet packet analysis and particle swarm optimization is proposed. First, wavelet packet transform is used to denoise the collected original signals. Then the processed approximation signal is divided into training data and test data. Training data is used to train fuzzy neural network, and particle swarm is used to optimize network parameters. Finally, test data is used to predict the dissolved oxygen and test the performance of the prediction model. Through comparison, the effectiveness of particle swarm optimization and wavelet packet transform is proved respectively. When predicting dissolved oxygen, based on wavelet packet transform, compared with BP algorithm, such error indexes as RMSE, MAPE and MAE of particle swarm optimization are decreased by 22.75%, 3.97% and 22.86% respectively; based on particle swarm optimization, compared with absence of wavelet packet transform, the three indexes are decreased by 16.82%, 3.36% and 16.65% respectively for existence of wavelet packet transform. The study shows that wavelet packet analysis and particle swarm optimization can improve the prediction accuracy, and the combined model can effectively predict dissolved oxygen.