In order to improve efficiency, reduce training costs, and promote the use of computers to replace the pilot seat in control simulation machines, an ensemble learning strategy was used to generate pilot response instructions. Five large-scale pre-trained language models were selected for fine-tuning, and four models with good performance were selected as the basic models for building the integrated learning model using K-fold cross-validation. The constructed integrated learning model achieved the best performance in this field on the air traffic control instruction dataset. According to the general recall-oriented understudy for gisting evaluation (ROUGE) evaluation criteria, the latest effects of R_OUGE-1 = 0.998, R_OUGE-2 = 0.995, R_OUGE-L = 0.998 are obtained. R_OUGE-1 focuses on the matching of individual words between the reference text and the generated text. R_OUGE-2 concentrates on the matching of two consecutive words, while R_OUGE-L is concerned with the matching of the longest common subsequence. In order to overcome the limitations of the general indicators in this field and more accurately evaluate the performance of the model, a set of keyword-based evaluation criteria for generated response instructions was proposed. This evaluation standard calculated various keyword indicators based on the results of segmentation of control texts to evaluate the effect of the model. Under the keyword-based evaluation criteria, the constructed model achievesan overall accuracy of 0.987, with aircraft call sign accuracy of 0.998.