To mitigate the impact of interference clutter in the detection of infrared small targets within complex backgrounds, a dual-neighborhood local weighted contrast algorithm is proposed. First, considering the background characteristics of small targets of different sizes, a dual-neighborhood window strategy is employed to effectively capture target and background features. Subsequently, directional information feature and weight coefficient enhancement maps are computed separately. The former fully utilizes the dispersal direction information of the target, while the latter generates weight information by utilizing the intensity and dispersion of grayscale responses in different regions. The combination of these two maps through image fusion results in a target saliency map. Finally, adaptive threshold segmentation is applied to extract targets from the saliency map. Comparative evaluations were conducted on four publicly available datasets with different backgrounds, involving six different algorithms. The proposed algorithm demonstrates robust anti-interference capabilities and accurate detection performance.