To address issues such as uneven illumination in coronary angiography images, low contrast between vascular structures and background regions, and the complexity of coronary vascular topology, we establish a coronary angiography vascular segmentation annotation dataset. Additionally, we propose a coronary angiography image vascular segmentation model based on the feature map pyramid. On the basis of the U-Net architecture, this model was improved and optimized. First, the first convolutional layer in the U-Net encoding part was replaced with a 7×7 convolutional layer to increase the receptive field of each layer. Modified ConvNeXt blocks were added to the encoding and decoding layers to enhance the network's ability to extract deeper-level features. Second, a Group Attention (GA) mechanism module was designed and incorporated at the U-Net skip connection to strengthen the features extracted from the encoding part, addressing semantic gaps between the encoder and decoder. Finally, a Pyramid Feature Concatenation (PFC) module was designed at the U-Net decoder, which fused features from different scales. Squeeze-and-Excitaton (SE) attention mechanisms were added to each layer of the PFC to filter out effective information from the feature maps. The loss function of the network is weighted based on the outputs of the PFC module at each layer, serving to supervise the feature extraction process across different layers of the network. The test results of this model on the test set are as follows: the Dice coefficient is 0.8843 and the Jaccard coefficient is 0.7926. Experimental results indicate that this model is highly robust in coronary vascular segmentation, more effectively suppressing noise under low contrast and achieving better segmentation results for coronary vessels when compared to other methods.