Obtaining a panoramic view of the seafloor through image stitching is of great significance for understanding deep-sea topography and geomorphology. Due to the challenges posed by the deep-sea environment, seafloor image features are often blurred, making the continuous stitching of sequential images require a stable and efficient stitching network. To address the issue, this paper proposes a deep-sea sequential image stitching network called AP-LG, which combines an improved ALIKED with LightGlue. Firstly, Deformable ConvNets v2 is used to replace the original deformable convolutional networks in ALIKED, introducing an adjustment mechanism to enhance the network′s feature capture capability. Then, multi-scale feature fusion is achieved through feature pyramid networks, improving robustness of the network to environmental changes. Finally, LightGlue is employed as the core feature matching network, and based on homography estimation strategies, continuous alignment and stitching of multiple sequential images are achieved. The experimental results indicate that on the UIEBD and DISD datasets, the AP-LG network achieved matching rates of 32.91% and 49.41%, respectively, enabling 86.00% and 93.60% of the image pairs to be matched with over 100 valid feature points. The proposed method can stably extract seafloor image features, achieve feature matching, and effectively complete the stitching of sequential seafloor images.