Objective: This research centers on the automated welding of medium and thick plates within large structural components. It is mainly focused on the in-depth exploration of the multilayer and multipass automated layout technology based on laser vision sensing. The aim is to solve a variety of crucial problems that occur during the welding process of medium and thick plates in large structural parts. By enhancing the welding quality and efficiency, it promotes the intelligent development of welding technology. Through a series of theoretical analyses, algorithm research and development, as well as experimental verifications, a multilayer and multipass automated layout technology based on laser vision sensing, along with its corresponding adjustment system, has been successfully developed. Methods: Under intricate and challenging welding conditions, the precise identification and feature extraction of weld seams were achieved, which laid a solid foundation for the automated tracking control of robots. A highly efficient solution based on a deep learning model was developed, and the end-to-end laser centerline extraction was successfully realized. This algorithm, while ensuring stable adaptation to a large number of working conditions, has a lightweight parameter scale and a relatively fast computing speed. Moreover, both its accuracy and efficiency can meet the engineering requirements, and it can accurately locate the key feature points in the laser weld images of different stages of multilayer and multipass welding of medium and thick plates. By obtaining the images of adjacent weld passes at the same position and using the laser centerline or the inflection point at the top of the groove as the matching feature to perform relevant operations, the weld pass morphology and features were extracted and served as the basis for the multilayer and multipass layout planning. Meanwhile, considering its self-correlation with the changes in welding process parameters, the real-time prediction of welding process parameters was achieved, thereby providing a basis for the rational selection of parameters at different positions of the welding path. Through an extensive analysis of welding parameters across various positions and plate thicknesses, strong correlative relationships among test plate types, plate thicknesses, process parameters, and weld bead deposition amounts were established. This enabled the effective planning of the number of welding layers and deposition quantities for medium and thick plates, providing a clear guideline for subsequent welding operations. Results: By incorporating visual sensing to identify weld seam information, the proposed technology facilitates the automatic planning of welding paths for each pass. Moreover, it determines crucial details such as the optimal position, attitude, and oscillation width of different weld beads at specific positions and times. Based on the deposition amount of each planned pass, process parameters are adaptively adjusted. Simultaneously, by combining position information and visual tracking, the position and orientation of the welding torch are adjusted accordingly, ensuring that the welding torch remains in the ideal state throughout welding. The system accounts for various influencing factors, including welding deformation, groove shape changes, and fluctuations during welding operations. Welding layout information and parameters are continuously refined and dynamically adjusted in real time. Conclusions: This adaptive mechanism ensures stable and efficient welding operations on large structural components, considerably enhancing production efficiency and product quality. Ultimately, this research plays a vital role in promoting the intelligent development of the manufacturing industry.