Multi-Feature Extraction Method of Power Customer?s Portrait based on Knowledge Map and Label Extraction

In order to realize the visualization of power customer characteristics and better provide power services for power customers, a multi-feature extraction method of power customer's portrait based on knowledge map and label extraction is studied. The power customer's portrait construction model is designed, which uses the knowledge map construction link to collect the power customer related data from the power system official website and database, and clean and convert the data; In the multi-feature analysis section, natural language processing technology is used to analyze the characteristics of power customers through Chinese word segmentation, vocabulary weight determination and emotion calculation; Based on the feature analysis results, the portrait label is extracted to generate the power customer's portrait. The power customer's portrait is used to realize the application of power customer's feature visualization, power customer recommendation, power customer evaluation and so on. The experimental results show that this method can effectively construct the knowledge map of power customers, accurately extract the characteristics of power customers, generate labels, and realize the visualization of power customer's portraits.