Judicial information extraction aims to identify fine-grained key elements in judicial documents， helping legal professionals efficiently manage large volumes of paperwork. Compared to general domains， elements in judicial documents are typically longer and semantically more dispersed， while fine-grained requirements place particularly strict demands on local detail extraction， making the model capable of handling long-range dependencies and precisely capturing fine-grained local semantic information. To address this challenge， a judicial element extraction method integrating global and local semantics was proposed. Firstly， element labels were concatenated with the content of judicial documents， and deep embeddings were generated using the BERT （Bidirectional Encoder Representations from Transformers） model. Secondly， a self-attention mechanism was introduced to enhance the model's comprehension of global context， while an adaptive multi-head attention mechanism dynamically adjusted attention weights to better capture rich， precise semantic features at the local level. Finally， to improve the model's generalization performance in identifying element boundaries， a combined loss function was designed that incorporated binary cross-entropy and KL （Kullback-Leibler） divergence with Gaussian-smoothed boundaries. Experimental results show that compared with sequence labeling methods， span-based extraction methods， and other methods， the proposed method achieves improvements in the F1 score on both the LAIC2023 and CAIL2021 legal element extraction datasets. Specifically， it outperforms the second-best model， DiffusionNER， by 2.88 percentage points on the LAIC2023 dataset， and on the CAIL2021 dataset， it outperforms the second-best Machine Reading Comprehension （MRC） model by 1.01 percentage points.