<p>With the explosive data growth on the web, there are massive textual data without class labels such that zero-shot text classification has attracted much research attention. However, existing zero-shot text classification models still take the class labels as the weakly supervised signal, which are usually unavailable in the open domain. In this paper, we study the text classification problem in a fully zero-shot setting, in which not only are we not given any training samples for unseen classes, but also the label names and the total number of unseen classes are unknown. We propose a fully zero-shot text classification model (FZeroTC) in a semi-supervised learning framework to simultaneously discover and label unseen classes. In the FZeroTC model, a pairwise loss and a Kullback–Leibler divergence-based regularization term are specially designed for unseen class discovery, and a faraway loss is specially designed for class labeling. We propose three different kinds of learning strategies based on the pretrained language model and prompt learning to train FZeroTC. From extensive experiments on four public text classification datasets, FZeroTC outperforms the state-of-the-art zero-shot text classification models in terms of unseen class discovery performance and can provide high-quality labels for unseen classes.</p>