Dairy products are important nutritious food items in people’s daily life. To improve the analysis of dairy product quality and safety risk prediction, the randomness, fuzziness, and incomplete information of detection products and inspection data obtained during dairy product evaluation in different regions were divided into grades using an improved softmax formula and processed in boxes according to the natural day. The risk grades were determined from the risk weight and other proportional mapping methods, and the dairy gray data were used to determine the potential risks in the qualified inspection data. Wavelet decomposition combined with the long short-term memory (LSTM) model was used to predict the risk of dairy product detection data in different regions. The results showed that the combined model exhibited an average accuracy of 97.54% and a standard deviation of 0.03, indicating higher accuracy and better stability compared to those of the empirical mode decomposition-LSTM model and wavelet decomposition-LSTM model with selective reconstruction and an interval of 2. Thus, risks associated with dairy product can be predicted and prevented. These results provide a reference and technical support for supervising risks associated with dairy products.