Effective classification of food safety regulatory issues is the key to the realization of the food safety regulatory question and answer system. In order to improve the effect of single label text classification, a multi-label text classification method based on bidirectional encoder representational from transformers-label embedding attentive model (BERT-LEAM) was proposed according to the different food safety perspectives and levels involved in the problem. A multi-angle and hierarchical multi-label labeling method was used to assign multiple labels to a single question text, and the pre-training language model of BERT was introduced to represent the context feature information. The dependency between the label and the text was learned by attention mechanism, the word was processed by embedding aggregation, and the tag was applied to the text classification process. The experimental results showed that the classification effect on the coarse-grained multi-label data set was better than that on the fine-grained multi-label data set. The method of text feature representation by BERT model was better than that of Word2Vec. The F1-W values of coarse-grained multi-label data set and fine-grained multi-label data set were 93.35% and 79.81%, respectively, which was better than other classification methods model. The problem classification based on food safety regulations question answering system was realized effectively by using the method of BERT-LEAM classification, which laid the foundation for the implementation of the follow-up question answering system.