The data distributions of cross-scenario images of strip steel defects vary considerably due to imaging factors such as camera type, parameters, and environmental illumination, resulting in poor generalization performance of defect recognition models based on deep learning. To address this issue, we propose a pseudo label correction and optimization domain adaptation (PLCODA) model for strip steel defect recognition. Firstly, a Retinex image enhancement module based on maximum entropy and brightness constraints was designed to generate an intermediate domain that is consistent with the label information in the source domain while different from the data distribution in the two domains. Second, we develop a dual-prediction adversarial coupling architecture that performs adversarial learning between the target domain and each of the source and intermediate domains to generate initial pseudo-labels for target-domain samples. Finally, we propose a pseudo-label correction and iterative purification strategy: we correct pseudo-labels via an improved noise matrix, then iteratively purify them by reinforcing high-confidence predictions, self-punishing low-confidence predictions, and reducing the discrepancy between pseudo-labels and ground-truth labels using a designed label-difference metric. The method was validated on steel-strip defect datasets from Handan Iron & Steel Group and the publicly Severstal Steel Defect Dataset. Experimental results show that the proposed method is superior to the existing domain adaptation methods for cross-scenario defect recognition.