Fully exploring the value of user data to identify user energy quality needs and promoting differentiated value-added services are of great value for improving user satisfaction and the competitiveness of power grid companies. Firstly

based on multidimensional user data

user profile theory to establish a multi-dimensional high- quality power user profile label system to identify users’power quality needs is introduced. Secondly

K-means clustering algorithm is used to profile high- quality power user groups

achieve user group division

quantify user power quality requirements based on prospect theory

and based on this differentiated value-added service package design is carried out. Finally

the effectiveness of the proposed method is verified through case analysis.