With the development of natural language processing technology, the technique of text summarization has been widely applied in various aspects of life. In the judicial field, the text summarization technique can be assisted by judicial texts to achieve "dimension reduction, "which is of great help in quickly understanding case details and obtaining case elements, promoting the development of judiciary towards informatization and intelligence. However, the existing summary generation models, when applied to judicial texts, the quality of the generated summaries is unsatisfactory, and there are problems such as repeated and redundant generation, inconsistency with the actual situation, especially when the perpetrator faces multiple charges and multiple penalties, the summaries generated by common summarization models will result in mismatched accusations and penalties. To address these issues, a judicial text summarization generation model based on knowledge-enhanced pre-training models legal case summary generation based on enhanced language representation with informative entities (LCSG-ERNIE) was proposed. The proposed model integrated judicial knowledge into the pre-trained language model and combined the idea of contrastive learning to generate summaries, thereby improving the quality of the generated summaries and reducing instances of mismatched accusations and penalties. Ultimately, through experimentation, it is demonstrated that the proposed model achieves good results.