Land use vector polygons serve as crucial data reflecting the status and spatial distribution of land use. However， with the acceleration of urbanization， manual-based methods can no longer meet the current demands for precise and efficient detection of changes in land use vector polygons Therefore， this paper combines deep learning technology， considering its requirement for a large volume of samples， and proposes an automatic change detection method for land use vector polygons under vector boundary constraints. Firstly， an improved simple linear iterative clustering algorithm is guided by the T1 vector polygons to accurately segment two periods of high-resolution imagery. Secondly， a high-quality dataset is constructed using an automatic sample generation and purification technique based on superpixels. Subsequently， an improved bilinear convolutional neural network is applied for T2 image classification. Finally， by statistically analyzing the proportion of land use type changes within the T1 vector polygons， the change detection results for land use vector polygons are derived. The experimental area is located in the Yangxi River area of Huishan District， Wuxi City， where the precision and recall rates of our method reached 87.2% and 96.1%， respectively， outperforming methods based on vector polygon feature statistics and change pixel statistics. This demonstrates the ability of our method to accurately and automatically locate changed land use vector polygons， showing broad application prospects in curbing the “non-agriculturalization” of arable land and inspecting illegal buildings.