Research on extended rule model for electricity marketing operation scenarios based on improved K-means feature clustering

To address the limitations of insufficient user segmentation accuracy and strategy rigidity in traditional electricity marketing, this study proposes an AI-driven framework integrating deep clustering and adaptive decision-making. A hybrid model combining deep embedded clustering and reinforcement learning (RL) has been developed to automatically extract high-dimensional behavioral patterns and generate dynamic marketing strategies. The framework introduces a feature attention mechanism for adaptive feature selection and employs a multi-agent RL system to optimize personalized strategies based on real-time user feedback. Experimental results on simulated datasets demonstrate a 23.5 \(\%\) improvement in clustering purity and a 31.8 \(\%\) increase in strategy response rates compared to conventional methods, validating the effectiveness of AI-enhanced components.