Location layout of aircraft assembly is an important factor affecting product quality. Most of the existing re-searches use the combination of finite element analysis and intelligent algo-rithm to optimize the location layout, which are limited by numerical simulation accuracy and the selection and improvement of intelligent algorithms. At present, the analysis and decision-making technology based on field data is gradually applied in aircraft manufacturing. Based on the percep-tion data of intelligent assembly unit of aircraft parts, a regression model of multi-input and multi-output support vector machine with Gauss kel function as radial basis function is established, and the hyperparameters of the model are optimized by hybrid particle swarm optimization genetic algorithm (PSO-GA). GA-MSVR, PSO-MSVR and PSOGA-MSVR model are constructed respec-tively, and their results show that PSOGA-MSVR model has the best performance. Finally, the design of the aircraft wing location layout is taken as an example to verify the effectiveness of the method.