A printed circuit board (PCB) defect detection algorithm based on multi-scale fusion optimization is proposed to address the low accuracy of traditional detection algorithms, which struggle with small surface defects resembling background features. Building on YOLOv8, a Swin Transformer module is integrated at the end of the backbone network's feature fusion layer to capture global information and enhance the understanding of both detailed and overall features. A global attention mechanism is embedded in the backbone to focus on target areas and reduce background interference. The WIoU loss function replaces the original CIoU, incorporating differential weighting to improve regression performance for small targets and complex backgrounds. Comparative experiments are conducted using different algorithms on the PCB_DATASET and DeepPCB datasets. The proposed algorithm improves detection accuracy by 3.64 and 2.42 percentage points on the PCB_DATASET and DeepPCB datasets, respectively, significantly enhancing defect recognition accuracy.