<p>Protein phosphorylation, a pivotal post-translational modification mechanism, plays essential roles in cellular signaling and disease regulation. While O-phosphorylation has been extensively investigated, the biological significance of N-phosphorylation has only recently gained attention, with its study hindered by inherent challenges including site instability and detection limitations. To address these challenges, we present NphosNet, a deep learning framework with four technical innovations. First, we constructed a novel, class-imbalanced N-phosphorylation dataset comprising pH-913, pK-2060, and pR-1700 subsets. For comprehensive feature representation, we developed a hybrid embedding strategy combining amino acid tokenization with positional encoding, enhanced by ProtT5 and EMBER2 pre-trained model embeddings to capture deep semantic information from protein sequences. Architecturally, we introduced a three-branch framework integrating Transformer modules, optimized xLSTM blocks, CNN components, and spatial attention-enhanced ResNet units for multi-dimensional feature extraction. A novel weighted three-channel cross-attention mechanism was specifically designed for effective feature fusion across branches. Comparative evaluations demonstrate NphosNet’s superior performance in N-phosphorylation site prediction (pH/pK/pR), achieving AUC values of 0.9227, 0.9099, and 0.9377 respectively, significantly outperforming existing methods. This advancement provides a robust computational tool for elucidating N-phosphorylation mechanisms in cellular processes and disease pathogenesis.</p> Graphical Abstract <p></p>