In medical image registration based on deep learning, when the medical image contains multiple tissue types, the structural difference between different tissue may lead to a decrease in the accuracy of network registration, especially in complex deformation regions, such as the junction of tissue and the lesion region, and accurate registration becomes more difficult. The existing registration algorithms have low registration accuracy for complex deformation regions. At the same time, the existing registration network cannot capture the local and global spatial information of the image at the same time, resulting in insufficient robustness of the network and low accuracy when it is transferred to other organs for registration. In order to solve the above problems, this study?creates a cascaded block registration model based on multi-spatial information extraction. This model can effectively use the local and spatial information of input images, divide medical images into blocks through block fusion technology, and perform fine registration for each image in turn to generate corresponding deformation field blocks. In the last stage of the model, the generated deformation field blocks are fused and restored to enhance the registration strength of the network for the local complex deformation region. The experimental results show that the proposed method not only improves the registration of the brain but also performs well in the registration of other human body parts, which improves the accuracy and reliability of medical image registration and provides better diagnosis and treatment support for clinicians.