Communication enterprise group customer member identification method, storage medium and computer equipment

Abstract

The invention discloses a communication enterprise group customer member identification method, which comprises the steps of performing feature engineering according to basic training data to obtain key features; selecting training data from the basic training data according to the key features, and training an identification model by using the training data; evaluating an identification effect ofthe identification model according to a preset index, and adjusting model parameters according to an evaluation result until the evaluation result reaches a preset value so as to obtain an optimal identification model; and according to the key features, performing communication enterprise group customer member identification on newly added service data by utilizing the optimal identification model. According to the invention, the key features which have important influence on identification are obtained through feature engineering, the blindness of feature selection is avoided, and the identification quality, performance, effect and identification efficiency are further improved. The invention further discloses a storage medium and computer equipment.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to a method for identifying customer members of a communication enterprise group, a storage medium and computer equipment. Background technique [0002] In the communication enterprise group customer market, there are current situations such as false customer information and the coexistence of true and false group members. Therefore, how to effectively identify the authenticity of existing group members, so as to carry out targeted maintenance for real group members, and improve customer service quality at the same time , to reduce the operating cost of enterprises has become an urgent problem for communication enterprises to solve. [0003] The existing communication enterprise group customer member identification has the following problems: [0004] The feature selection of existing methods is mostly based on common sense assumptions. That is, for member identification, so...

AI Technical Summary

Problems solved by technology

That is, for member identification, some commonly used fixed features are selected according to fixed basic common sense (for example: subscription to VPNM, group unified payment, group ring tone, mobile phone mailbox, roaming, terminal type, etc.), but the features selected based on common sense are not necessarily Affects the distinguishing features of recognition, resulting in a reduction in the quality of recognition

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