One of the main challenges in judicial artificial intelligence is the identification of key case elements. The existing methods only take the identification of case elements as an identification task of named entities, and thus, the recognized information is mostly irrelevant. In addition, due to the lack of effective use of global and local information in texts, the effect of element boundary recognition is poor. To solve these problems, this study proposes a recognition method of key case elements by integrating global and local information. Specifically, the BERT model is used as the input-sharing layer of judicial texts to extract text features. Then, three sub-task networks of judicial case element recognition, judicial text classification (global information), and judicial Chinese word segmentation (local information) are established on the sharing layer for joint learning. Finally, the effectiveness of this method is tested on two public data sets. The results show that the F1 value of the proposed method exceeds the existing optimal method, improves the classification accuracy of element entities, and reduces boundary recognition errors.