随着无人机技术的快速发展，无人机数量与空域需求显著增长，无人机进入融合空域成为趋势。为改进传统的“人在回路”空管模式，提高融合空域的管控效率，对深度学习方法用于空管指令解析进行了研究。根据空管用语的特点对指令进行整理、扩充、分类和标注，编写了可用于自然语言理解模型学习的空管指令数据集。使用BiGRU-CRF模型作为深度学习的基础架构，加入注意力机制与意图反馈机制构建联合模型获取指令意图及指令参数。分别在空管指令数据集和ATIS数据集上进行了评估，结果表明模型在意图识别和槽填充任务较基础架构均有近1.5%的提升，证明论文方法具备实用性和有效性，为无人机自然语言指控技术的发展提供了有力支撑。

With the rapid development of UAV technology， the number of UAVs and airspace demand has increased significantly， which make it a trend for UAVs to enter integrated airspace. To modify the traditional ATC management pattern of “man in loop” and increase the control efficiency of fusion airspace， deep learning method is applied to air traffic control instruction parsing. According to the characteristics of ATC terms， this paper sorted， expanded， classified and annotated the instructions to write an ATC instructions dataset which can be used for learning of natural language understanding model. BiGRU-CRF model is used as the infrastructure of deep learning， and attention mechanism and intention feedback mechanism are added to construct a joint model to obtain instruction intention and instruction parameters. The evaluation results on ATC instruction dataset and ATIS dataset show that the model is nearly 1.5% better than the infrastructure in the task of intent identification and slot filling. The method presented in this paper is practical and effective， which provides strong support for the development of UAV natural language control technology.