Infrared small target detection aims to achieve pixel-level separation of small targets from the background in infrared images, with significant applications in military, security, and aerospace fields. However, due to low contrast and low signal-to-noise ratio, existing methods often lose edge information of infrared small targets and fail to effectively utilize the relationship between low-level and high-level features in infrared images. To address these limitations, this study proposes an edge-guided and cross-fusion method for infrared small target detection. Specifically, to overcome the shortcomings of existing methods in extracting edge information, this study constructs an edge-guided feature extraction module. This module integrates edge information into the global-local and detail features of the image through attention weighting, thereby utilizing edge information of small targets more effectively. Additionally, to better fuse high-level and low-level features of the image and enhance the target-background separation capability, this study designs a dual-branch cross-fusion module. This module processes low-level and high-level features of the image through spatial attention and channel attention, respectively, and fully utilizes the complementary relationships between different levels of features through cross-fusion. The experimental results on two benchmark datasets show that compared with state-of-the-art methods, this method improves the IoU metric by 1.89% and the nIoU metric by 2.28%.