<p>Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods.</p>