The broad-learning-based dynamic fuzzy inference system (BL-DFIS) can automatically assemble simplified fuzzy rules and achieve high accuracy in classification tasks. However, when BL-DFIS works on large and complex datasets, it may generate too many fuzzy rules to achieve satisfactory identification accuracy, which adversely affects its interpretability. In order to circumvent such a bottleneck, a fuzzy neural network called feature-augmented random vector functional-link neural network (FA-RVFLNN) is proposed in this study to achieve excellent trade-off between classification performance and interpretability. In the proposed network, the RVFLNN with original data as input is taken as its primary structure, and BL-DFIS is taken as a performance supplement, which implies that FA-RVFLNN contains direct links to boost the performance of the whole system. The inference mechanism of the primary structure can be explained by a fuzzy logic operator (I-OR), owing to the use of Sigmoid activation functions in the enhancement nodes of this structure. Moreover, the original input data with clear meaning also help to explain the inference rules of the primary structure. With the support of direct links, FA-RVFLNN can learn more useful information through enhancement nodes, feature nodes, and fuzzy nodes. The experimental results indicate that FA-RVFLNN indeed eases the problem of rule explosion caused by excessive enhancement nodes in the primary structure and improves the interpretability of BL-DFIS therein (The average number of fuzzy rules is reduced by about 50%), and is still competitive in terms of generalization performance and network size.