Welding defects not only bring several economic losses to enterprises and individuals but also threatens peoples lives. We propose a deep learning model, where the data-trained deep learning algorithm is employed to detect the weld defects, and the Convolutional Neural Networks (CNNs) are utilized to recognize the image features. The Transfer Learning (TL) is adopted to reduce the training time via simple adjustments and hyperparameter regulations. The designed deep learning-based model is compared with other classic models to prove its effectiveness in weld defect detection and image recognition further. The results show this model can accurately identify weld defects and eliminates the complexity of manually extracting features, reaching a recognition accuracy of 92.54%. Hence, the reliability and automation of detection and recognition is improved signifificantly. Actual application also verififies the effectiveness of TL in weld defect detection and image defect recognition. Therefore, our research results can provide theoretical and practical references for effificient automatic detection of steel plates, cost reduction, and the high-quality development of iron and steel enterprises.Index Terms - convolutional neural network, deep learning, image detect recognition, transfer learning, weld defect detection