To address the challenges of severe data missingness， high uncertainty in influencing factors， and limited prediction accuracy in flight ground service milestone event prediction， a flight ground service milestone event prediction framework named CTrans-ATGCN was proposed， including two core components： missing data imputation and milestone event prediction. First， based on the Transformer architecture， a convolutional layer was introduced to extract the temporal and topological features in flight ground service milestone event data， and a self-attention mechanism was employed to model potential global dependencies among different milestone events， thereby constructing CTrans （Convolutional Transformer）， a method for flight ground service data imputation that integrates convolutional self-attention mechanism. On this basis， a milestone event prediction method， Attention-based Temporal Graph Convolutional Network （ATGCN）， was constructed. In this method， a Temporal Graph Convolutional Network （T-GCN） was utilized to capture spatio-temporal dependencies in historical milestone event data， and by combining an attention mechanism with a scoring function， the weights among different milestone event nodes were adjusted dynamically， so as to achieve high-precision prediction for ground service milestone events. Experimental results on a 2023 real-world dataset from an airport in North China show that in a non-random missing scenario with a 70% missing rate， the proposed imputation method reduces the Mean Absolute Error （MAE） by approximately 14.91% compared to the suboptimal model GraphSAGE （Graph Sample and AggreGatE）； on low-missing-rate datasets， the proposed “imputation + prediction” method achieves prediction performance similar to that of Sliding Bi-Directional Graph Convolutional Neural network Model （SBDGCNM）； on datasets with 70% missing rate， CTrans-ATGCN achieves the MAE and Root Mean Square Error （RMSE） of 14.76 and 18.42， respectively， outperforming all baseline methods. It can be seen that this framework provides an effective method for flight ground service prediction under missing data， and offers reliable support for efficient scheduling of ground personnel and equipment.