A Mining Algorithm for Power Grid Customer Behavior Subject Information based on Big data

The power grid customer behavior subject information is a commonly seen behavior, but because of the differences of individuals, customer behavior subject ways and effects are often different. This paper selects sixteen secondary indicators, and has self-development ability, thinking ability, learning and applying ability, and incentive for power grid customer behavior subject information as the primary indicators. The results show that the accuracy of the algorithm constructed in this paper is close to that of big data mining technology, both of which are more than 80%, which is superior to traditional data analysis methods. The reason is that mining technology increases the synergy coefficient of different dimensions, improves the weight and convergence factor. At the same time, the evaluation values are distributed evenly, and the maximum evaluation value obtained is very good or good, indicating the secondary indicators in the hierarchical system of this paper are reasonable.