Most of the existing predictive models are empirical models which contain too many parameters without biological explanations. In this study, a non-empirical growth prediction model based on neural network was proposed. A BP neural network secondary growth model was established by using Listeria monocytogenes as an example, using the temperature, pH value and Aw value of the experimental environment. The growth rate and double time of microbes were fitted in different environments. Subsequently, combining with the initial concentration of microorganisms, the primary model of microorganism growth with time was predicted. Finally, the growth data of Listeria monocytogenes were tested, and the experimental results showed that the model could predict the growth period of microbes. Compared with the empirical model, this non-empirical prediction one was more suitable for predicting the microbial growth dynamics, and also the parameters of the empirical model could be solved effectively.