Infrared small target detection is crucial for various applications, including surveillance and remote sensing. A common issue in this field is the loss of target information during the downsampling process, which can significantly impact detection accuracy. To address this challenge, this paper proposes a multi-attention fusion network (MAFNet), which primarily consists of two core modules: the Patch-aware Parallel Reconstructive Attention (PPRA) module and the Frequency Non-local Sparse Dimension Perception (FNSDP) module. The PPRA module enhances the encoder’s feature extraction capability through multi-branch feature extraction and a multi-channel attention parallel mechanism. The FNSDP module improves the model’s detection performance by fusing features from different dimensions, effectively preserving target details during multiple downsampling processes. We evaluate MAFNet on several public datasets, including NUAA-SIRST, IRSTD-1K, and NUDT-SIRST, and the experimental results demonstrate that MAFNet outperforms the current state-of-the-art methods across various detection metrics, highlighting its effectiveness and feasibility.