Objective Multi-modal magnetic resonance imaging MRI is often used in clinical practice for diagnosis and brain tumor segmentation due to its containing differential and complementary information. However in real-world scenarios complete modalities of brain MRI may not always be available and the absence of one or more key modalities can lead to a decrease or even collapse in segmentation performance. To address this issue a segmentation network incorporating an attention-based decoupling and reconstruction module is proposed to maintain the segmentation accuracy for brain tumors even in the presence of missing modalities. Methods The network adopted a U-shaped architecture and integrated a decoupled and reconstitution module based on attention mechanisms. In this decoupling and reconstitution module high-dimensional features from each modality were first fused to obtain common basic features. Subsequently by employing a differential attention mechanism individual features unique to each modality were extracted from the shared common basic feature. Finally basic features and individual features were integrated through cross-attention and fed into the decoder to obtain the final segmentation results. Additionally a learnable skip connection gate was designed to allow the shallow features of multiple modalities in the encoder to interact sufficiently before being passed to the decoder.Results In a series of experiments the segmentation network demonstrated excellent performance on the BraTS2020 brain glioma dataset successfully segmenting brain tumors even under missing modalities. The evaluation metrics showed an improvement of over 2% compared with other advanced methods reaching up to 85. 3%. Conclusion Therefore the attention-based brain tumor segmentation network can effectively segment brain tumors in incomplete modalities which is of great practical significance.