The exponential growth of internet usage and social media has inundated us with an immense volume of unannotated textual content. Annotating such vast amounts of data requires considerable time and expertise, making it a daunting task. While handling massive datasets in an unsupervised manner offers certain advantages, the challenges inherent in effectively grouping this data underscore the importance of text clustering. To address this challenge, we introduce TextNet, an end-to-end deep learning model. Unlike traditional methods, TextNet does not rely on external tags or labels. Instead, it leverages the assumption that different clusters exhibit variations in their distributions–samples within a cluster share similar distributions, while samples from different clusters display distinct variations. The innovation of TextNet lies in its utilization of dual models: one processes the original text input, while the other handles augmented samples generated to resemble the input. Through unsupervised training, TextNet employs the Clussimloss loss function to iteratively refine the model, leading to more effective text clustering. We extensively evaluated TextNet on two widely used standard text datasets. The experimental results demonstrate that our proposed method surpasses existing models in terms of clustering accuracy and effectiveness.