Nowadays, food safety issues have been concerned by both governments and consumers. However, the increasing number of food safety related articles makes it difficult to retrieve useful information from the articles in a short time with high accuracy. In order to improve the efficiency and accuracy of accessing food safety information, a question answering system was proposed, which was based on long shortterm memory (LSTM) and information retrieval techniques. The system relied on the food safety unstructured texts obtained by Web crawler technologies, and question answer pairs were selected by using Lucene, and LSTM was used to predict answers according to matching degrees between question and candidate sentences. Based on Lucene’s retrieval mechanism and the LSTM model, a question answering system was built which can select sentences that were most likely to contain the answer to given questions. The results showed that the proposed system outperformed the baseline which was only based on retrieval mechanism. Moreover, the performance analysis were made for the two methods with respect to the numbers of candidate articles and candidate sentences.