Abstract: Focusing on the defects of current assembled artificial neural network(ANN) models, its weak generalization ability for engine experiment sample data of different array structure, multi-step linear interpolation method(MLIM for short), a new assembled ANN modeling method, was put forward, which was based on finite element method. In MLIM, using one-dimensional input vector with abundant sample data, some mesh lines were set up to make a division of the input space. The sample data on these mesh lines was brought in BP neural model training process, from which some high-precision artificial neural network functions were obtained. Output of sample data between meshing lines was multi-step linearly interpolated by the most two neighboring mesh line ANN function value. Compared with traditional assembled neural network modeling methods, MLIM has good adaptability in processing multi-dimensional engine dynamic characteristic testing data with different input array length.