Key Information Extraction of Food Environmental Safety Criminal Judgment Documents Based on Deep Learning

Food has an impact on everyone's daily life, the long-term stability of the nation, human survival and development, people's lives and health, and the steady advancement of society. A food safety criminal judgment is a legal document used to record the trial of food-related offences. It primarily contains the case's history, information about the parties involved, and the verdict. In order to identify defendants, their charges in court documents, and other important court information, this paper proposes a method for extracting key information from food safety criminal conviction documents based on deep learning. It builds and analyses a hidden Markov model (HMM) based on the corpus of crime-related components, and uses the model trained by a DL neural network to determine the trend of a given data set. In the test result classification task, the results demonstrate that the Transformer model can achieve macro accuracy rates of roughly 0.963, macro recall rates of 0.932, and macro F1 scores of 0.958. Experiments demonstrate the model suggested in this paper's performance and effectiveness in extracting abstract information from food criminal trial documents.