On the basis of improved hyperbolic tangent sigmoid transfer function, a cause-factors evaluation BP neural network model for estimating the atmospheric corrosion of steels was proposed. Using the zero mean stand method to preprocess the input data, Bayesian regularization arithmetic was introduced to solve the generalization problem on sparse data. The simulated results showed that the model provided good evaluation for atmospheric corrosion cause-factors without any prior knowledge.