The invention relates to a management line loss abnormity identification method based on data mining. The technical points comprise: step 1, carrying out sub-sequence segmentation on preprocessed management line loss time sequence data by adopting a sliding window method; step 2, constructing a time sequence prediction model based on a neural network, obtaining a predicted value of a management line loss sub-sequence, and judging the sub-sequence of which the difference range between the predicted value and an actual measurement value is greater than a preset threshold value as an abnormal sub-sequence; step 3, extracting characteristic variables of the abnormal sub-sequences, establishing a management line loss characteristic sample set, and clustering by adopting three different algorithms; and step 4, performing cluster matching on the three clustering results, obtaining a final clustering result by adopting a majority voting clustering integration method, and comparing the difference between the number of objects in the cluster and a preset threshold value to obtain a specific classification condition of the management line loss abnormal sub-sequence. The abnormal condition ofline loss can be quickly and accurately identified and managed, and better stability and practicability are achieved.