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China's hub airports have been busy for a long time, and the high load brings the risk of distorted information interaction. Speech recognition technology can be used to assist decision-making, however, the special characteristics of control speech and sample size limitations make it difficult to apply traditional deep learning techniques directly to the ramp control field. To address this problem, a speech recognition method based on small sample learning is proposed. Firstly, a data enhancement method is proposed to further improve the model effect by combining a priori domain knowledge and constructing a generative adversarial network based on a data generation strategy group to enhance the acoustic model recognition ability; then, some structures and parameters of the acoustic model are reconstructed; finally, acoustic modeling features from a general speech library are applied to the recognition of ramp control speech commands by a migration learning method. The experimental results show that the method reduces the word error rate to 6.14%. This research can be applied to the detection and recognition of ramp control speech commands in advanced ground activity guidance and control systems in airports, which can help modern airports operate with high quality.