The invention discloses an
urban construction archive abnormity early warning method based on
artificial intelligence, and the method comprises the steps: S1, collecting and preprocessing multi-source archive data, and constructing an archive feature
library; s2, extracting file text features, drawing component features and approval
label features, and generating a multi-
modal feature vector; s3, generating an archive
knowledge graph and a
time sequence feature sequence by using the multi-
modal feature vector, inputting the archive
knowledge graph and the
time sequence feature sequence into an HGT graph neural network, and outputting archive structure embedded representation and evolution chain semantic features; s4, the archive structure embedding is compared with the version evolution chain feature, and a potential abnormal
record is generated; s5, carrying out
risk quantification based on the improved MC-Dropout, and outputting an abnormal type, a position and a
risk level; and S6, submitting the abnormal information to an
urban construction archive management platform. According to the method, the early warning strategy is optimized through the graph neural network and the uncertainty evaluation mechanism, and efficient and accurate
urban construction archive anomaly recognition and early warning are achieved.