Simulation Research on User Electricity Consumption Feature Model Based on Data Mining Algorithms

China's electricity companies have played an important role in ensuring the safety of the electricity market and electricity customers, playing the main role of acceptance feedback, resulting in inaccurate modeling results and affecting the safety and stability of the power grid. This article constructs a user electricity consumption feature model based on the DM (Data Mining) algorithm and conducts simulation research on it. Analyzed the electricity consumption periods of holiday residents and non holiday residents, and extracted features. The research results showed that residents' daily electricity consumption had certain characteristics, but the distinction between workdays and holidays was not obvious. The peak electricity consumption is concentrated between 19:00 and 22:00, during which most residents have already returned home, generating a large amount of daily electricity. The trough of electricity consumption is concentrated between 7:00-8:00, during which most users are in a sleeping state and generate very little electricity for daily use. These data and information often have the characteristics of large volume and high processing difficulty, and the connections between each data are relatively close. From this, it can be seen that studying the analysis and interaction methods of residents' electricity consumption behavior with massive DM has positive social significance.