The large-scale integration of distributed photovoltaic (PV) generation and electric vehicles (EVs) increases uncertainty in distribution-network source and load conditions and complicates system operation. Conventional optimization methods based on iterative solutions and day-ahead forecasts may not meet the real-time operating requirements of active distribution networks. Therefore, a multi-time-scale Graph Attention Network (GAT)-based online optimization and control method is proposed for distribution networks with emerging source-load resources. First, stochastic and time-varying source-load scenarios are generated using a PV output model and an EV charging-load model. Second, the reactive power optimization model is solved offline to construct a control-decision dataset that accounts for the different dynamic responses of the regulation devices. The GAT is then trained to learn the nonlinear mapping from distribution-network operating states to control decisions, forming a multi-time-scale online decision-making model. The long-time-scale model regulates discrete devices, whereas the short-time-scale model regulates continuous devices. For the IEEE 33-bus case study, the average GAT inference time is 0.0703 s, compared with 83.7667 s for PSO. The hour-level model achieves MAE/RMSE values of 0.0398/0.0407, while the 15 min model achieves 0.0374/0.0397; all bus voltages are maintained within 0.95–1.05 p.u. after optimization. The model is validated under the tested operating distribution; broader robustness and field applicability require further validation.