Abstract: Abstract: The aim of the study was to develop a growth/no-growth interface model to predict the growth probability of Vibrio alginolyticus associated with lightly salted Pseudosciaena crocea under 3 environmental factors, and to explore the inhibitory effect of environmental factors on the growth kinetics of target micro-organism. The effects of pH value, water activity (aw) and NaCl content on the growth probability of the Vibrio alginolytica were studied at ambient temperature (25 ℃). At present, mainly the "fence technology" is used to change the growth environment of microorganisms by changing the water activity, salt, acetic acid, Nisin and sugar, so as to achieve the role of inhibition to microorganisms. Logistic regression is a commonly used method for simulating the microbial growth boundary (growth/non-growth interface) in food, through which the growth environment can be adjusted and the shelf life can be extended. Artificial neural network model PNN (probabilistic neural network) is a feed forward neural network with strong nonlinear pattern classification ability and high accuracy of nonlinear algorithm, which can solve the growth/non-growth interface problems, and the PNN has simple structure and high training speed without considering the complex chemical reaction during storage. Simple logistic equation, second-order linear logistic regression equation and PNN artificial neural network model were used to establish the growth/non-growth interface model of Vibrio alginolyticus, while fraction correct (FC) and false alarm rate (FAR) were used to compare the goodness of fit of the 3 models. The Gompertz model was used to fit the growth condition, and the growth kinetics parameters were obtained. The results showed that the second-order linear logistic regression equation had better fitting results, the consistency index of the training set was 94.8%, and that of the validation set was 90.9%, while the consistency index of the PNN artificial neural network was 95.6% and 90.0% for the training and validation set, respectively. The FAR of the second-order linear logistic regression equation was 5% (training set) and 0 (validation set), while that of the PNN artificial neural network was 6.6% (training set) and 22% (validation set). The effects of the environmental factors were as follows: With the increase of salt content, the growth/no-growth boundary obviously moved to low water activity and low pH value. In the same salty condition, in the range of high aw and high pH value, the growth rate was higher and the retardation period was shorter. With the increase of salt content, even under low aw such as 0.91 and 0.90, the Vibrio alginolyticus also began to grow slowly, but there was a long lag time. The conclusions are obtained: PNN artificial neural network can do quick classification prediction on the growth/no-growth data of Vibrio alginolyticus in the industrial production, and the second-order linear logistic regression can evaluate the stability of aquatic products under the conditions of aw, pH value and salt content. By constructing the probabilistic models and kinetic models of Vibrio alginolyticus which can assess the stability of characteristic aquatic products in the range of pH value, aw and salt content, it can provide the guide to suppress microorganisms without the use of chemical preservatives to ensure quality and safety of pickled Pseudosciaena crocea.