Scoliosis is a common spinal disease in the current society. Therefore, it is important for doctors to diagnose the degree of spinal curvature to quickly and accurately locate the spinal bone corners on X-ray images and calculate their Cobb angles. In light of the occlusion of other organs and complex background interference in orthopaedic X-ray images, a neural network model based on the embedded attention mechanism, vector loss module, and vertebra-focused landmark detection (VFLD) network is proposed. The rotary attention mechanism module is embedded between the encoder and decoder to enhance the network's extraction of the deep and high-dimensional features of the spine bone, inhibit the interference of other organs, and allow the use of the vector similarity loss function to train the network. The experimental results show that the accuracy of the symmetrical mean absolute percentage error of the proposed model in the MICCAI 2019 open spine challenge dataset is as high as 9.31, and can effectively improve the ability of the original model to detect vertebral corners. Compared with many existing models, the proposed model has a higher accuracy and robustness.