Abstract: The screening of legal popularization cases is a critical mission in building a smart justice system.Traditional manual screening is costly and inefficient,while existing text classification methods struggle to accurately discern fine-grained elements related to educational value,such as key facts,judgment results,and social hot spots,thus limiting identification accuracy.To address this challenge,a legal popularization case dataset is constructed and an element-aware pre-trained model is proposed.Speci-fically,the dataset is built via cross-platform data collection and regular expression matching,clarifying the structured distribution of elements and partitioning them into three complementary semantic spaces:fact view,judgment view,and label view.A fact encoder exploring core disputes,a judgment encoder extracting warning features,and a label encoder capturing hierarchical dependen-cies are respectively utilized to extract deep features.Moreover,a multi-view feature fusion module is developed,employing independent routing and shared experts to achieve dynamic fusion of key elements across different views.Experimental results on the self-built dataset demonstrate that the proposed model effectively improves the accuracy and robustness of legal educational value identification,providing a feasible auxiliary solution for automated screening.

Key words: Natural language processing, Legal popularization cases, Pre-trained language models, Mixture-of-experts