Recently, deep learning-based methods have been widely applied in deformable medical image alignment tasks. Among them, utilizing novel loss functions and effective network architectures to improve registration performance is a common approach. This article proposes a multiscale feature perception loss and attention module ECA-D, which improves the design approach of using only mean square error (MSE) or normalized cross-correlation (NCC). Inspired by the current popular large language model (LLM), this paper trains a classification neural network using multi-site medical image data and constructs a multi-scale feature learning process to improve the accuracy of the classification network. Subsequently, a multi-scale perceptual loss function is designed to enhance the accuracy of registration. To improve the learning ability of the alignment network, a new attention module ECA-D was designed to more effectively utilize spatial and channel information. After training on the LPBA40 dataset, our model showed a 3% improvement in Dice score on the untrained Neurite OASIS compared to the most advanced methods. The experimental results show that compared with other popular registration methods, our method has higher registration accuracy and better robustness.