Class A and B butt welds of pressure vessels are important stress-bearing parts, and the measurement of their quality parameters is an important part of welding quality evaluation. This article studies the detection method of weld quality parameters of pressure vessels based on deep learning active vision. A calculation method for weld parameters is proposed under the coexistence of multiple defects, which breaks through the problem that the weld quality parameters are difficult or impossible to calculate under the coexistence of weld defect parameters. We carry out the structural design of the encoding-decoding image feature point extraction network (EDE-net), which can better realize the one-time and accurate extraction of weld surface parameter feature points. We study the deep network structured channel pruning method to effectively improve the real-time performance of pressure vessel weld detection. Taking the welds of pressure vessels of different sizes as the experimental objects, the results show that the EDE-net network with the backbone of Resnet50 has CR= 0. 5 as the overall compression rate of the model, and the extraction time of a single image is reduced from the original 0. 31 s to 0. 19 s, a reduction of 38. 7% . The test report is given by the third-party testing agency, and the device simultaneously measures 5 parameters of the butt weld (Class A, B) weld, which takes less than 0. 63 s, and the allowable error of the measurement error is ≤0. 08 mm.