The invention provides a multi-stage content quality evaluation method and
system based on a large
language model, and relates to the technical field of
natural language processing. The method comprises the steps of obtaining a to-be-evaluated content
data set and an evaluation index corresponding to a
data type of the to-be-evaluated content
data set; based on the evaluation indexes, utilizing a first large
language model to carry out batch multi-dimensional coarse screening on the content
data set, obtaining evaluation scores of all content data, sorting the evaluation scores, and filtering and retaining a plurality of content data with the top scores; an improved ELO dynamic scoring mechanism is introduced, a second large
language model is used for conducting dynamic pairwise comparison sorting on the filtered content data, final ELO scores of all the content data are obtained and sorted, and an ELO
score sorting result serves as a content quality sorting result. According to the method, a large language model is utilized to break through the cognitive depth
bottleneck, evaluation objectivity is improved by relying on a dynamic game mechanism, two-stage architecture is combined to balance calculation efficiency and sorting precision, and finally a content quality evaluation
universal solution capable of being rapidly adapted to multiple fields is formed.