The application relates to the technical field of intelligent evaluation of
medical education, in particular to a text-guided intelligent evaluation method for clinical skills based on a
ranking graph constraint. The method comprises the following steps: segmenting and extracting features from a clinical skill video, pre-
processing the video, combining video
time sequence information and action speed text embedding to construct a
time sequence feature representation, introducing skill classification text and a learnable memory embedding vector, realizing multi-
modal feature decoding based on a cross-attention mechanism, outputting a skill
score prediction value through a scoring network, constructing a
ranking relationship matrix by using real
score differences, introducing a
ranking graph constraint loss and a scoring loss for joint optimization, and thus improving the accuracy and ranking consistency of skill evaluation. The application realizes accurate modeling of a clinical skill operation process and effective discrimination of relative levels between samples, thereby significantly improving the accuracy, consistency and reliability of skill evaluation.