<p>Legal Judgment Prediction (LJP) is a research hotspot in legal intelligence, which aim to predict the judgment result based on the fact description. The existing research mainly focuses on multiclass classification methods and single-label learning by analyzing case facts, but neglect the semantic correlation between the fact descriptions and legal keyword labels, and the significance of legal keywords, which leads to unsatisfactory judicial prediction results. To address this limitation, we introduce a novel framework for LJP that enhances the utilization of legal concept keywords through prompt learning. We propose a method based on legal charge keywords and prompt engineering to enhance the performance for LJP. Our approach first integrates legal keywords with fact descriptions to improve the representation capacity of case fact vectors. We have incorporated the legal keywords into the language model to enhance the model’s ability to understand and process legal texts. And we design a prompt template to guide the reasoning process of the pre-trained language model through structured instructions. This strengthens the semantical relevance between the fact description and legal labels, so that the model can more accurately capture the logical connection between fact descriptions and charges. Experimental results on CAIL2018 datasets across different tasks show an improvement in F1 scores ranging from at least 1.59% to a maximum of 9.28%, illustrating the effectiveness of our method compared with the state of the art models such as LADAN, NeurJudge and CL4LJP.</p>