Abstract:Improving the analyzing and understanding ability of the customer service system for group customers' electricity consumption problems seems to be one of the important ways to improve the quality of customer service for power industry. Based on clustering technology in data mining, this study establishes a customer service data analysis clustering model for customers' electricity consumption problems recorded by a customer service center, and then proposes an improved adaptive feature weighted K-Means clustering algorithm for the analysis of electricity consumption problems. The experimental results show that the proposed method can quickly and accurately realize the automatic clustering of customer service data and mine the hidden critical information of customers' electricity consumption problems, thus providing technical support for improving the quality of customer service and predicting the potential risk of customer service.