The invention provides a fuzzy reasoning-based single-class classification method and classifier, which fuzzifies a feature vector, generates a fuzzy rule set and corrects the fuzzy rule set. The specific method is as follows: after a target rule set is generated, rule correction needs to be carried out if the same judgment condition appears but the judgment results are different. And the single-class classifier can accurately identify abnormal samples. Firstly, data processing is carried out, then fuzzification is carried out on the data, then a fuzzy rule set is established, and rule correction is carried out. After the rule set is established, a test sample can be used for testing the rule set, and when the rule generated after the sample of the test set is subjected to data processingof the same mapping is different from the existing rule, the sample is considered as an abnormal sample and then classified into an unknown category. According to the method, the number of data set samples in the aspect of single-class classification is increased. Meanwhile, a better classification effect can be achieved by applying a more optimized algorithm, and the training speed is increased.