The application relates to an industry classification and
abnormality identification method, device,
electronic equipment and storage medium. The industry classification and
abnormality identification method comprises the following steps: S1, sample screening, screening registered telephone numbers of a plurality of enterprises in an industry; S2, continuously learning
algorithm calculation, applying a
machine learning
algorithm to extract communication information records of sample objects, continuously tracking and training industry / enterprise sample communication characteristics; S3, learning result correction, selecting to-be-audited
correction number objects in a random sampling manner, adopting multi-point correction, obtaining industry information and enterprise information of the number objects, judging and identifying the industry to which the number objects belong, and correcting inconsistent cases based on the judgment result; S4, abnormal fluctuation detection, timely finding and detecting abnormal fluctuations of different normal behaviors of enterprise / number objects, and being used for management and early warning. According to the industry classification and
abnormality identification method, abnormal fluctuations of different normal behaviors of enterprise / number objects can be timely identified and found in advance.