In this study, a novel weak infrared small target detection network based on sparse attention and multiscale feature fusion is proposed to address the challenges of low pixel occupancy and limited texture features for weak infrared small targets within complex backgrounds, leading to difficulties in feature extraction, low detection rates, and high false alarm rates. The network utilizes the segmentation attention of ResNest to extract features at different scales. A BiFormer attention module is introduced to learn the distant relationships between targets and backgrounds. Furthermore, a fusion module is employed to merge both high- and low-level features, with the final detection results represented as a binary image through a head module. The experimental results demonstrate that the proposed method achieves the best performance in terms of both Intersection over Union (IoU) and F-measure. Compared with the dense nested attention network (DNANet), the proposed method improved the IoU by 3.9% and F-measure by 5.6%. Compared with the attentive bilateral contextual network (ABCNet), the proposed method improved the IoU by 5.8% and F-measure by 10%. Moreover, the proposed approach exhibited robustness and adaptability in effectively detecting small weak infrared targets in diverse, complex backgrounds. This method is applicable to weak infrared small-target detection in complex backgrounds, exhibiting superior performance.