New Explorations in Legal Research through Big Data Technologies
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Abstract
Traditional legal research methods originated before the era of large-scale data. Although they remain suitable for case-by-case analysis, they are less effective when applied to large, heterogeneous, and continuously updated datasets. This study explores new legal research methods enabled by big data technology, focusing on legal pattern recognition, compliance risk assessment, intelligent retrieval, and knowledge graph construction. The Legal-BERT prediction model achieves 89.3% accuracy in case outcome prediction tasks, while a three-dimensional compliance risk index based on the analytic hierarchy process reaches a coverage rate of 88.4%. Sustained monitoring through big data mechanisms reduces the overall system risk index by 35.0%. In addition, the combination of the Legal-BERT-enhanced retrieval system and the Legal-Gen document summarization tool improves research efficiency by 74.5%. An art copyright knowledge graph reaches an expert overlap rate of 83.7% in related regulation discovery tasks. These results indicate that big data technology acts as a methodological accelerator for legal research rather than a substitute for legal judgment. It makes knowledge production more systematic and efficient, and provides a replicable methodological reference for computational jurisprudence, digital compliance governance, and legal practice innovation in data-intensive engineering and platform environments.
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