Addressing the challenge posed by the low contrast between the choroid and sclera in choroid segmentation， this research introduces the TransGLnet choroid automatic segmentation network， employing an attention mechanism. The incorporation of a Global Attention Module （GAM） within the convolutional layer involves matrix multiplication between features， establishing nonlinear interactions across multiple features in the global spatial context. This enables the extraction of global features without an excessive number of parameters. To explore local features， a Local Attention Module （LAM） is introduced between the convolution layer and Transformer encoder， focusing on a 1/4 feature graph. The movement rule for feature graph elements maintains row position consistency while rearranging elements in the column position from largest to smallest. The integration of these two modules ensures that the network effectively considers both global and local features. Experimental results showcase the efficacy of the proposed TransGLnet network with a Dice value of 0.91， accuracy at 0.98， equal crossover ratio of 0.89， F1 value reaching 0.90， and a Hausdorff distance of 6.56. Comparative analysis against existing automatic choroidal segmentation methods reveals notable improvements in performance metrics. The network presented in this study demonstrates robustness and stability， rendering it suitable for clinical reference.