In this paper, a TSK-type compensatory neural fuzzy network (TCNFN) for classification applications is proposed. The TCNFN model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 3 of the TCNFN model contains adaptive compensatory fuzzy operations, which make fuzzy logic systems more adaptive and effective. Furthermore, a self-adaptive learning algorithm, which consists of the structure learning and the parameter learning, is also proposed. The structure learning is based on the degree measure to determine the number of fuzzy rules and the parameter learning is based on the gradient descent algorithm to adjust the parameters of the TCNFN. The advantages of the proposed method are that it converges quickly and that the fuzzy rules that are obtained are more precise. The performance of TCNFN compares excellently with other various existing methods.