The construction of well−facilitated farmland in China has imposed higher requirements on the digital and refined management of farmland, posing new challenges for the automatic extraction of multi−element information from remote sensing data. This study proposes a framework for the automatic extraction of multiple key farmland elements by integrating prompt engineering with remote sensing feature knowledge. Leveraging the general segmentation capabilities of the vision foundation model, combined with open−source data and feature−driven specialized algorithms, the proposed approach enables efficient automatic identification of critical farmland elements, including plots, field roads, shelterbelts, and irrigation and drainage facilities. Taking the well−facilitated farmland construction area in Shouguang City, Shandong Province as a case study, we conducted technical validation using high−resolution domestic satellite remote sensing imagery. Experimental results demonstrate that the proposed method can achieve batch automatic processing within farmland project areas, significantly reducing dependence on large amounts of high−quality training samples and manual workload. Moreover, it shows good generalization capabilities across images with different acquisition times, spatial resolutions, and regions, thus greatly improving data production efficiency and practical application potential. This research provides a novel approach for the intelligent interpretation of digitalized farmland areas and offers strong technical support for the application of remote sensing in post−construction supervision of agricultural engineering projects.