The introduction of modern power supply service system has raised higher requirements for the service quality of electricity customer service. Accurate power supply service traffic prediction not only improves the quality of power customer service, but also effectively reduces the cost of customer service personnel. Therefore, this paper proposes a short-term traffic prediction method for power grid based on Adaboost and convolutional neural network (Adaboost-CNN) and a value-added service correction method. First, the isolated forest algorithm is used to identify the abnormal data, and the Lagrange interpolation function is applied to repair the abnormal data or missing data. Next, the analytic hierarchy process is employed to quantify user information, meteorological data, and power outage details. The grey correlation method is then used to analyze the influence factors of traffic volume, and these factors are incorporated as inputs to the traffic volume prediction model. An Adaboost algorithm is applied to integrate multiple CNN models, resulting in an Adaboost-CNN traffic prediction model. Finally, considering the value-added services within the power supply service system, the prediction results of the model are corrected to obtain the final traffic prediction value. The case analysis shows that the proposed forecasting model reduces prediction error by an average of 11.05 percentage points compared to a single forecasting model and by 5.32 percentage points compared to a combined forecasting model, demonstrating better forecasting accuracy.