A Study of Legal Judgment Prediction Based on Deep Learning Multi-Fusion Models—Data from China

Abstract

Legal judgment prediction implies predicting judgment results based on the case description suitable for judgment predictions in legal terms, crimes, fines, and punishment. This study examines Chinese legal cases, focusing on legal predictions based on deep learning multiple fusion models. It aims to analyze case descriptions and predict legal terms and fines. The use of artificial intelligence models to predict the law can solve the problem of increasing workload of legal institutions and personnel because of the increasing number of cases; and simultaneously, reduce the differences in judgment. This will improve the efficiency and fairness of legal judgments. This study used the BDCI2017 dataset for experiments and applied deep learning algorithms to improve the prediction accuracy. Various models, such as TextCNN, TextRNN, Wide & TextCNN, and TextDenseNet classify cases and fines. Results revealed that TextDenseNet is better than the other model structures in terms of predictive accuracy.