In order to establish a shelf-life prediction model that can be applied to a variety of fresh aquatic products at the same time. Using back propagation (BP) neural network model， genetic algorithm-back propagation(GA-BP) neural network model， radial basis function(RBF) neural network model， extreme learning machine (ELM) neural network model and support vector machine for regression (SVR) model to predict the remaining shelf life of four aquatic products respectively. First measure 4 indicators of tuna， salmon， turbot and sea bream at 0， 4 ℃ and 10 ℃， in order to construct training samples and test samples. Including sensory score， total number of colonies， TVB-N value， K value， pH value. After correlation analysis， the pH indicator with the lowest correlation was eliminated， and other indicators were selected as input indicators for each model. Then， determine the network topology and parameters of each model， and train each model. Finally， this study used each model trained to predict the remaining shelf life of the test sample. The result showed that the order of model prediction accuracy was: SVR prediction model > RBF neural network model > GA-BP neural network model > ELM neural network model > BP neural network model. The prediction accuracy of the BP neural network model was the worst， mean square error (MSE) was 9.5127×10-4， mean absolute error (MAE) was 0.0197， mean absolute percentage error(MAPE) was 0.0825， R2 was 0.9766. The prediction accuracy of the SVR prediction model was the best， its prediction error was within 12%， MSE=2.2971×10-4， MAE=0.0128， MAPE=0.0631， R2=0.9944， it could well predict the remaining shelf life of 4 aquatic products. This study provides a theoretical basis for the quality control of aquatic products.