Abstract: Based on BP neural network theory, the food safety research was carried out with the daily food inspection data from Chinese General Administration of Quality Supervision,Inspection and Quarantine. Firstly, the inspection data was simplified to 167 supervised items which had the most direct relation with food safety forecast. Then the BP neural network model was established with input layer of the previous 167 items, five groups as output layer, and two hidden layers as passing function. Last, the model was trained and validated by the simplified dataset. The research showed that the model could effectively remember and identify characteristics of food inspection datasets and then make effective forecast for new dataset. This will be beneficial for research methodologies and techniques in Chinese food safety warning practice.