Objective N6-methyladenosine (m6A), the most prevalent epigenetic modification in eukaryotic RNA, plays a pivotal role in regulating cellular differentiation and developmental processes, with its dysregulation implicated in diverse pathological conditions. Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development. However, traditional experimental methods are time-consuming and costly. Although various computational approaches have been proposed, challenges remain in feature learning, predictive accuracy, and generalization. Here, we present m6A-PSRA, a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways. The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity. This ensures that the biological context conveyed by nucleotide order is retained. A bidirectional long short-term memory network (BiLSTM) then processes the encoded matrix, capturing both forward and backward dependencies between bases to resolve contextual correlations. The second branch employs a k-mer tokenization strategy (k=3), decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns. A pre-trained Doc2vec model maps these subsequences into fixed-dimensional vectors, reducing feature dimensionality while extracting latent global semantic information via context learning. Both branches integrate residual networks (ResNet) and a self-attention mechanism: ResNet mitigates vanishing gradients through skip connections, preserving feature integrity, while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction. This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans, mice, and rats, m6A-PSRA consistently outperformed existing methods in accuracy (ACC) and area under the curve (AUC), achieving >90% ACC and >95% AUC in every tissue tested, indicating strong cross-species and cross-tissue adaptability. Validation on independent datasets—including three human cell lines (MOLM1, HEK293, A549) and a long-sequence dataset (m6A_IND, 1 001 nt)—confirmed stable performance across varied biological contexts and sequence lengths. Ablation studies demonstrated that the dual-branch architecture, residual network, and self-attention mechanism each contribute critically to performance, with their combination reducing interference between pathways. Motif analysis revealed an enrichment of m6A sites in guanine (G) and cytosine (C), consistent with known regulatory patterns, supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features, achieving high prediction accuracy and robust generalization across tissues and species, providing an efficient computational tool for m6A methylation site prediction.