In order to improve the self-learning capability of intuitionistic fuzzy Petri nets(IFPN), a novel parameters
optimization method is proposed, in which the back propagation algorithm of neural net is introduced to the parameters-
optimized process of IFPN. By constructing the approximate continuous function of transition firing and intuitionistic fuzzy
reasoning, the method makes the parameters get rid of the dependence upon experience, which makes the parameters adjust
the fact instance better. Meanwhile, the IFPN model can own better generalization performance and self-adjusting ability,
and the reasoning results are more accurate and reliable as well. Finally, the classical instance verifies the effectiveness and
superiority of the proposed method.