The current state evaluation of electricity meters mostly evaluates the operation status of electricity meters from an isolated and micro perspective, and does not conduct overall evaluation of electricity meters in the station area from a larger perspective. Evaluating the status of a single electricity meter in isolation may cause a misjudgment of the operational risk of the entire station area. In order to have a more comprehensive and detailed grasp of the operation status of the electricity meter and the station area, a hierarchical network model is proposed in this paper. The method is based on the historical data of the electricity acquisition system and the metering production scheduling platform. According to the status of each indicator, the quality, expected life and failure rate of the electricity meter are scored. According to the score, the status of the electricity meter is divided into five levels, including stable, normal, concerned, serious and early warning. Based on the abnormal event data of the electricity meter recorded by the terminal in the station area, the deep neural decision forests (DNDF) model is used to classify the abnormal event levels in the station area into four levels: serious, important, more important and general, and the model classification accuracy rate reaches 86.58%. Experimental results show that the proposed method can accurately determine the operating status of the electricity meter and the operational risk of the entire station area, creating conditions for the reliable and stable operation of the power grid.