The improvement of classification accuracy is currently hampered by the fact that most short text classification approaches suffer from inadequate information mining and insufficient attention to local information. In light of this, an enhanced semantic-syntactic point-wise graph convolutional network (ESS-PWGCN) short-text combination classification model with few samples and semi-supervised civil aviation was proposed. Firstly, the model selects training set high-confidence keyword information to enrich and enhance the expression of key information within civil aviation short texts, thereby broadening the applicability of the model. Secondly, it balances the influence weights of global-local information within the textual graph structure while learning the semantic-syntactic information of civil aviation short texts by combining point-wise convolution with graph convolutional networks (GCN) and multi-head attention mechanisms.Then, a fully connected layer is employed to amalgamate the acquired information for outputting classification results. Finally, experiments conducted on aviation datasets and other public domain datasets demonstrate that the ESS-PWGCN model not only surpasses the current state-of-the-art self-training text graph convolution networks( ST-TextGCN) model in terms of accuracy and F₁ score by 4.59% and 6.53%, respectively, but also exhibits superior robustness and generalizability.