Abstract: 【Objective】 This study aims to accurately acquire the crop planting structure and distribution using remote sensing data and deep learning methods in view of the complex crop planting structure. 【Methods】 A sample set was obtained through field investigations.Sentinel-2 and Sentinel-1 images were acquired using the Google Earth Engine cloud platform; Model training and validation for deep learning algorithms were conducted using Google Colab; Classification accuracy was improved by adjusting and optimizing relevant parameters of deep learning.Additionally, the accuracy of three classification methods—deep learning, random forest, and support vector machine—was compared. 【Results】 The deep learning approach achieved the highest classification accuracy, with an overall accuracy of 94.6%. 【Conclusion】 The utilization of deep learning algorithms enables precise monitoring of crop planting structure in Qitai County.