The rapid development of industries such as heavy machinery, shipbuilding, and energy equipment has led to a sustained increase in the demand for medium-thick plate welding. However, conventional welding methods still heavily rely on manual operations or robotic teaching, resulting in low efficiency and poor consistency in weld quality. These limitations make it difficult to meet the high precision and productivity requirements of modern intelligent manufacturing. To address these challenges, this study proposes an intelligent multi-layer, multi-pass welding system for medium-thick plates, integrating deep learning and adaptive control. First, a deep convolutional neural network (CNN) based on the ResNet101 architecture was developed to automatically classify weld groove types, achieving a classification accuracy of 99.62%. Second, groove feature points were extracted with sub-millimeter precision using a Gaussian Mixture Model (GMM) clustering algorithm, enabling accurate analysis of geometric parameters. Finally, a multi-layer, multi-pass welding process system was designed with three key optimizations: adaptive adjustment of welding sequence and torch pose, dynamic compensation for wire stick-out, and compensation for thermal deformation and scanning errors. Experimental results demonstrate the system’s feasibility and effectiveness in industrial applications, significantly improving the level of welding automation. Manual intervention time was reduced by over 91.3%, and welding efficiency increased by 30%. This work offers a practical and engineering-ready solution and establishes a technical paradigm for the intelligent transformation of welding processes under the framework of smart manufacturing.