In this paper, the detection data was derived from 50,000 batches of meat products sampling inspection by the China Food and Drug Administration, from 2015 to 2018. A Long Short-Term Memory (LSTM) neural network model for lead content in meat products was constructed to analyze the current status of meat product safety and the types of risks. Six levels of food safety risk in lead were established by combining the national food safety standards as a reference with experts scoring. Then the food safety risk data were processed by softmax and hanning window. The Tensorflow was applied to establish a three-layer LSTM neural network of time series risk early warning model through 500 rounds of model training. The results showed that the LTSM had a high accuracy for risk warning in lead content, the data from 31 provinces basically matched the actual risk value, with an average error of 0.27. The model was a stable and reproducible method for forecasting as well, with the average error of ten runs is 0.27, which could provide a new method to implement the trend early-warning in different regions of the country and provide a technical support for government and related authorities for daily supervision.