Abstract: Image information mining technology based on deep learning is one of the research hotspots of imagery interpretation and an important means of natural resource investigation and monitoring. Nevertheless, there are some problems, such as low rate of identification, large amount of manual workload and low generalization performance. In this paper, a change detection algorithm based on dual attention mechanism CD-DLinkNet is proposed. The sample data set of non-agriculturalization of cultivated land was constructed based on the results of geographical conditions monitoring. The problems of weak generalization and high misidentification performance in complex application scenarios were solved betterly. Experimental results represent that: for the detection of non-agriculturalization of cultivated land, the object recall of the dual attention mechanism CD-DLinkNet algorithm can reach 82.52% and the object precision can reach 77.02%, meeting the production needs of remote sensing monitoring projects and effectively assisting the detection and extraction of non-agriculturalization of cultivated land.