为满足星载相控阵天线对低成本以及低旁瓣电平的要求，提出一种基于深度学习的天线稀疏阵列优化方法。针对大规模稀疏阵列优化问题，传统遗传算法在优化过程中面临计算复杂度高、适应度评估耗时等问题。通过引入深度学习模型预测旁瓣电平，替代耗时的仿真计算，显著降低了时间复杂度。实验结果表明，与传统遗传算法相比，本文方法在优化效果上有一定提升，在面向高频次工程应用场景下具有计算效率优势，能够有效解决大规模稀疏阵列优化问题。

To meet the requirements of low-cost and low-sidelobe level for space-borne phased array antennas， an optimization method for sparse arrays based on deep learning is proposed. For the optimization problem of large-scale sparse arrays， traditional genetic algorithms face issues such as high computational complexity and time-consuming fitness evaluation during the optimization process. In this paper， a deep-learning model is introduced to predict the sidelobe level， replacing the time-consuming simulation calculations， and significantly reducing the computational complexity. Experimental results show that， compared with traditional genetic algorithms， the method proposed in this paper shows some improvement in optimization effect， exhibits superior computational efficiency particularly suited for high-frequency engineering applications and can effectively solve the optimization problem of large-scale sparse arrays.