A multi-training module Takagi-Sugeno-Kang (TSK) fuzzy classifier, H-TSK-FS, is proposed by means of reconstruction of training sample space. H-TSK-FS has good classification performance and high interpretability, which can solve the problems of existing hierarchical fuzzy classifiers such as the output and fuzzy rules of intermediate layer that are difficult to explain. In order to achieve enhanced classification performance, H-TSK-FS is composed of several optimized zero-order TSK fuzzy classifiers. These zero-order TSK fuzzy classifiers adopt an ingenious training method. The original training sample, part of the sample of the previous layer and part of the decision information that most approximates the real value in all the training layers are projected into the training module of the current layer and constitute its input space. In this way, the training results of the previous layers play a guiding and controlling role in the training of the current layer. This method of randomly selecting sample points and training features within a certain range can open up the manifold structure of the original input space and ensure better or equivalent classification performance. In addition, this study focuses on data sets with a small number of sample points and a small number of training features. In the design of each training unit, extreme learning machine is used to obtain the Then-part parameters of fuzzy rules. For each intermediate training layer, short rules are used to express knowledge. Each fuzzy rule determines the variable input features and Gaussian membership function by means of constraints, in order to ensure that the selected input features are highly interpretable. Experimental results of real datasets and application cases show that H-TSK-FS enhances classification performance and high interpretability.