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The application provides an IPv6 full-response prefix detection method based on an autoregressive model, which comprises the following steps: firstly, semantic learning is performed by using the autoregressive model to learn the complex relationship between the full-response prefix mode and related factors, and the learned probability model is finally used to generate the representation of the full-response prefix mode; then, in the prediction stage, the possible full-response prefix mode is predicted according to the semantic information of the routing prefix, and the candidate full-response prefix is generated; finally, in the scanning stage, the candidate full-response prefix is scanned online, the detection results are collected, and whether the candidate full-response prefix is a real full-response prefix is judged according to the detection results; the granularity correction is performed on the real full-response prefixes, the prefix set with a real length is obtained, and a full-response prefix database with wide coverage and high accuracy is constructed.