The invention relates to the technical field of student intelligent questioning and answering, and discloses a student intelligent questioning and answering method and
system based on a large
language model and rapid retrieval, and the method comprises the steps: combining a student portrait to generate question vectors, rejecting
noise points of a question vector set, obtaining hot questions, and storing the hot questions into a hot
knowledge base after the hot questions are expanded by experts; generating a summary of the knowledge document by using a large
language model, and adding the summary to each text block; generating a block summary for each text block, and storing the block summary and the text blocks into a universal
knowledge base; inputting questions proposed by students into the large
language model to generate a thinking chain; for sub-questions needing to be retrieved, firstly retrieving the hotspot
knowledge base, if the sub-questions are not matched with the hotspot knowledge base, retrieving the block summary of the general knowledge base, and obtaining corresponding text blocks through mapping to obtain retrieval results; and guiding the big language model to fuse the thinking chain and the
retrieval result to generate a structured answer. According to the method, the
summary information is introduced after the text blocks are segmented, so that the
information loss in the
text segmentation process is reduced, and the reasonability of knowledge base construction is improved.