The main advantage of this algorithm is its high efficiency in terms of time complexity, but its steps are relatively complex, and it is not widely used. The Vatti algorithm [20] and the Greiner-Hormann algorithm [21] are recognized algorithms that can clip arbitrary polygons in a limited time and obtain correct results [22]. The Greiner-Hormann algorithm uses a bilinear linked list data structure and has a lower algorithmic complexity than the Vatti algorithm [23]. The clipping process is the same between the different operators, and only the resulting polygon outputs are different. Therefore, the Greiner-Hormann algorithm intersection operator is selected here for parallel GPU optimization. As the scale of geospatial data continues to expand, polygon overlay analysis algorithms are becoming both more computationally intensive and more data intensive [24; 25]. Research on the parallel optimization of polygon overlay analysis algorithms has important practical significance for improving algorithm efficiency and perfecting GIS theory. At present, many scholars have carried out parallel optimization of polygon overlay analysis algorithms and achieved a degree of acceleration. [PERSON] et al. [26] demonstrated a practical polygon clipping method based on line segment trees, which parallelized the algorithm via OpenMP instruction. [PERSON] et al. [27] designed and implemented a practical distributed spatial analysis framework, which effectively improved the efficiency of parallel spatial analysis calculations for processing massive spatial data in a distributed environment. [PERSON] et al. [28] proposed a parallel spatial analysis framework that combines MySQL and MPI to perform parallel storage and processing analysis of vector data. [PERSON] et al. [29] used the MPI programming model in a Linux cluster environment and adopted the average striping method to perform point inclusion test operations. [PERSON] et al. [6] parallelized the intersection, union, difference, and XOR processing of overlay analysis in a multicore environment. They first filtered the clipping polygon and the clipped polygon using the minimum enclosing rectangle to exclude nonintersecting polygons. [PERSON] [30] classified and described in detail the characteristics of complex polygonal objects composed of single rings, multiple rings, and a large amount of node data under simple feature models, paying particular attention to how these objects form a multilink structure. On this basis, a set of polygonal object screening methods and parallel computing strategies based on multiple data bounding boxes were proposed and established. [PERSON] et al. [31] divided overlay analysis into two stages, namely, MBR filtering and polygon clipping, and applied a GPU. They improved the Weiler-Atherton algorithm by using a new intersection insertion method and a simplified algorithm for marking entry and exit points, combined with a parallel preprocessing algorithm, and proposed a GPU-based polygon clipping algorithm. Current research on parallelization methods for polygon overlay analysis algorithms has achieved certain results, but the calculation process in traditional polygon overlay analysis algorithms is tightly coupled with the data structure, making it difficult to achieve fine-grained parallelization [32, 33]. [PERSON] et al. [34] conducted fine-grained GPU parallelization on the Greiner-Hormann algorithm based on CUDA, but they only used the number of polygon vertices to measure the complexity of the data, and the experimental data were relatively small, and the thread modes were not diverse enough. The parallel algorithm in this paper effectively avoids its defects during optimization. To address the above problems, this paper improves the most time-consuming intersection calculation step in the Greiner-Hormann algorithm by using multithreaded parallelism in CUDA and presents two kernel functions that implement an improvement algorithm based on GPU parallelization.