The application provides a kind of automobile field entity recognition method and
system based on large
language model, the method extracts candidate entity in the text to be processed by first identification model, and the entity that cannot be mapped to standard
knowledge base is marked as pending entity;When there is pending entity, retrieve relevant documents from the automobile field
dynamic text library as supplementary knowledge, and conduct secondary entity recognition combined with large
language model to output the final recognition result;The application adopts a
hybrid knowledge base architecture, combines structured
database and entity vector
library to realize dual-channel retrieval, and supports dynamic incremental update based on the recognition result.The application effectively solves the problem of missed recognition caused by the strong timeliness and high
ambiguity of professional terms in the automobile field, significantly improves the accuracy and
recall rate of entity recognition, and at the same time, through the dynamic
knowledge updating mechanism, the
system's ability to quickly adapt to new car series and new brand release is guaranteed, which is suitable for application scenarios such as automobile
vertical search and intelligent
question answering.