  Legal charge prediction, an essential task in legal AI, seeks to assign
accurate charge labels to case descriptions, attracting significant recent
interest. Existing methods primarily employ diverse neural network structures
for modeling case descriptions directly, failing to effectively leverage
multi-source external knowledge. We propose a prompt learning framework-based
method that simultaneously leverages multi-source heterogeneous external
knowledge from a legal knowledge base, a conversational LLM, and related legal
articles. Specifically, we match knowledge snippets in case descriptions via
the legal knowledge base and encapsulate them into the input through a hard
prompt template. Additionally, we retrieve legal articles related to a given
case description through contrastive learning, and then obtain factual elements
within the case description through a conversational LLM. We fuse the embedding
vectors of soft prompt tokens with the encoding vector of factual elements to
achieve knowledge-enhanced model forward inference. Experimental results show
that our method achieved state-of-the-art results on CAIL-2018, the largest
legal charge prediction dataset, and our method has lower data dependency. Case
studies also demonstrate our method's strong interpretability.
