Analysis and Comparison of Limitation Interests in Civil Procedure Law in the Context of Information Technology

Abstract

This paper first discusses the application of text mining techniques in public security, with the research focus on case classification, feature extraction, and collusion analysis in civil litigation. A plain Bayesian-based first-level classifier is proposed to accurately classify first-level case texts. For all second-level case category document sets, keyword cooccurrence mapping is used to replace word frequency as a metric for feature selection, and a second-level classifier is constructed using a simple vector distance algorithm. Finally, the impact of time-barred benefit abandonment and time-barred benefit loss on the evolutionary system in civil litigation is investigated in depth using the pilot water tax reform in Hunan Province as a case study. The results show that when one of the subjects chooses to discard the statute of limitations benefits, x and y show a trend of steadily increasing convergence to 1 from the initial point, and the final stabilization point tends to (1,1). This paper aims to provide references and suggestions for reforming information and optimizing civil litigation.

Keywords

• text mining
• plain bayesian
• classifier
• civil litigation
• concurrent case analysis
• co-occurrence mapping
• 97p70