Abstract: By using the back propagation (BP) neural network, the relationship between car styling contours and
image semantics was constructed in MATLAB to quickly judge the vehicle side modeling style. Then the expression
recognition model built by the convolutional neural network (CNN) was employed to establish the automobile model
evaluation system, and to analyze and identify users’ preferences for the new design, thus obtaining the vehicle side
modeling scheme which can meet users’ emotional needs. Finally, the feasibility of the method was verified through
examples, and the optimal curvature range of the flow-type car was inferred. The experimental results show that the
quantitative evaluation method of automobile modeling based on neural networks can evaluate the product modeling
design more accurately and produce the side shape of concrete image in the form of data.

Key words: BP neural network, vehicle side modeling, imagery, facial expression recognition, user emotional needs