基于深度学习的法律判决预测研究综述OA

Legal judgment prediction(LJP),as a pivotal task in the domain of intelligent justice,focuses on employing natural language processing(NLP)technologies to deeply analyze legal texts,thereby accurately predicting the applicable legal articles,charge categories,and penalty outcomes of judicial cases.With the deep integration of artificial intelligence and the judicial field,the efficient and reliable LJP method has great practical significance to improve judicial efficiency and promote intelligent judgment.However,existing researches still exhibit significant limitations in technical approaches and theoretical frameworks,and studies that systematically summarize the core challenges and methodological innova-tions in this field are urgently needed.This study systematically outlines the operational workflow of LJP,encompassing input processing,encoding,prediction,and result generation.It delves into core challenges at each stage,such as limita-tions in input information,difficulties in handling long texts,and insufficient utilization of legal precedents.Furthermore,the research synthesizes corresponding mitigation strategies,including the construction of multi-task learning frame-works,the application of contrastive learning paradigms,and the exploration of interpretability enhancement approaches.Future research directions are also highlighted,such as multimodal information fusion,efficient processing of unstruc-tured texts,and optimization for few-shot learning scenarios.