In practical nonlinear system, due to the limitation of resources, the input signal is quickly refreshed, while the output signal is slowly sampled. Thus, it is difficult to identify the original nonlinear system by using the sampled data. For this purpose, the linear models of multiple characteristic points of nonlinear system are transformed into a series of consequent linear models of the fuzzy model by the lifting technique. On this basis, we propose a fuzzy identification algorithm based on competitive learning and recursive gradient descent method. And we prove that the parameters of the fuzzy model can be uniformly convergent under the condition of persistent excitation. In view of chemical pH neutralization process, the fuzzy model of the chemical system is established by using non-uniformly sampled data. By comparing the output errors between the actual data and the output data of the fuzzy model, it is shown that the fuzzy identification method can establish the process model in the real system under the condition of non-uniform sampling, which verifies the validity of the proposed method.