Although Takagi-Sugeno-Kang (TSK) is widely used in practically every profession, how to enhance its classification accuracy and interpretability is still a research focus. In this study, a deep TSK fuzzy classifier is proposed. This classifier (i.e., RCC-DTSK-C) can randomly select features and combine features and own triplely concise interpretability for fuzzy rules. There are several other varieties of RCC-DTSK-C such as reasonable structure for rule representation, namely, (1) the proposed RCC-DTSK-C consists of many base-training units and each base-training unit can be trained independently. According to the principle of stacked generalization, the input of the next base-training unit consists of the training set and random result obtained from random projections about prediction results of current base-training unit. (2) In RCC-DTSK-C, the hidden layer of each base-training unit is represented by triplely concise interpretable fuzzy rules which are in the sense of randomly selected features. These features are selected by dividing into the not-fixed several fuzzy partitions and randomly combining rules and keeping the same input space in every base-training unit. (3) The source data set is mapped into each of the independent base-training units as the same input space, which effectively ensures that all the features of the source data are preserved in each separate training unit. The extensive experimental results show RCC-DTSK-C can achieve the enhanced classification performance and triplely concise interpretability for fuzzy rules.