The invention relates to an industry
knowledge question-answering method and
system based on self-adaption and retrieval enhancement generation, and the method comprises the steps: obtaining input questions, employing a question classifier to judge the types of the input questions, and enabling the types of the input questions to comprise viewpoint type questions,
data type questions, and other types of questions; respectively adopting different retrieval strategies for viewpoint questions, data questions and other questions, and outputting question answers based on a
large model; and a scoring and
rewriting mechanism is introduced to
score and rewrite the answers to the questions based on the
large model, and final answers to the questions are output. According to the method, the user questions are classified, different retrieval methods are used for each class of questions, and scoring and
rewriting mechanisms are introduced, so that the
knowledge retrieval efficiency is improved, the computing resources required by retrieval are reduced, and the question and answer accuracy is improved; the problems that
knowledge base information is accurate but incomplete, the accuracy of internet information is insufficient, and knowledge conflicts possibly exist between the
knowledge base and
the internet information in an industry
knowledge question and answer scene can be solved.