The work aims to construct a new radial basis function (RBF) neural network shelf life prediction model. The microbial, physical and chemical indexes of oat fresh wet noodles at different storage temperatures were studied, and the main factors affecting the shelf life of oat fresh wet noodles were screened out through Pearson correlation analysis. The remaining shelf life of oat fresh wet noodles was predicted by models of microbial growth kinetics and the RBF neural network, respectively. The microbial growth kinetics model could not fit the change of the total bacterial count of oat fresh wet noodles very well, and the prediction accuracy was poor. On the contrary, the relative error between the predicted value and the actual value of the RBF neural network prediction model was 2.66%, which was very little. The RBF neural network prediction model is effective, which provides a certain reference for the future prediction of food shelf life.