| Due to the diversity and large quantity of material in aircraft assembly, there tend to be great uncertainties in just in time supply of material. To effectively solve the dynamic scheduling problem for aircraft moving assembly line with uncertain supply of material, the support vector data description (SVDD) in machine learning field is combined with the traditional scheduling method, and a dynamic scheduling approach based on SVDD is proposed. First, CPLEX and meta heuristic are used to solve the mathematical model under different material supply delay conditions, and the optimized results are taken as the samples to train the SVDD classification model. In the real time scheduling phase, the trained SVDD model is used to make the classified decisions on “advance”, “delay”, or “on schedule”. Based on the results of classification, a local look ahead searching method is presented to make a further decision on specific starting time of jobs advanced or delayed. The computational results prove that the proposed algorithm can meet the actual requirement of dynamic scheduling for aircraft moving assembly line in both response speed and solution effect.