According to the load balance problem of large-scale parallel vector polygon rasterization, a novel data decomposition method was proposed. Firstly, the number of polygon nodes or the number of polygons was employed to evaluate the amount of calculations of a subset. The spatial locations of decomposed lines were computed iteratively and the balanced calculations between decomposed subsets were guaranteed, so as to realize data decomposition and load balancing. Secondly, a binary-tree based fusion strategy was put forth to merge the polygons across multiple subsets. The proposed parallel algorithm was implemented under a multi-core CPU-based environment and multiple China land use datasets were employed. Experimental results show that the presented method can outperform conventional methods for different datasets and can achieve a higher speed-up ratio and good load balancing. Moreover, when dealing with a large-scale vector dataset, the number of polygonal nodes is more appropriate to be the metric to evaluate the calculation of a subset precisely.