Legal Impact of Digital Information Technology on the Chain of Evidence in Criminal Cases

Criminal evidence serves as the foundation for criminal proceedings, with evidence used to ascertain the facts of cases being critical to achieving fairness and justice. This study explores the application of digital information technology in building a data resource base for criminal cases, formulating standard evidence guideline rules, and optimizing evidence verification procedures. A named entity recognition model based on the SVM-BiLSTM-CRF framework is proposed, coupled with an evidence relationship extraction model using the Transformer framework to improve evidence information extraction through sequential features and global feature capturing. Results show that the F1 value for entity recognition in criminal cases reaches 94.19%, and the evidence extraction model achieves an F1 value of 81.83% on the CAIL-A dataset. These results are utilized to construct evidence guidelines, helping case handlers increase case resolution rates to approximately 99%. The application of digital technology enhances evidence collection efficiency, accelerates case closures, and offers a pathway to improving judicial credibility.