Multibehavior recommendation aims to leverage diverse user interaction types—such as views, clicks, and purchases—to better understand user intent and enhance recommendation accuracy. However, effectively modeling these heterogeneous behaviors remains challenging due to the sparsity, noise, over-smooth embedding, and dynamic nature of interaction sequences. To address these issues, we propose GCTMR (Graph Contrastive Transformer for Multibehavior Recommendation), a framework that integrates graph-based contrastive learning with a behavior-aware Transformer architecture. Specifically, the methodological contribution lies in three key innovations: 1) an adaptive sparse attention mechanism employing α -entmax that dynamically filters out noisy or irrelevant interactions to preserve the distinctiveness of user behavior representations; 2) a frequency-aware multibehavior encoder using FFT/IFFT decomposition that disentangles stable long-term preferences from short-term behavioral shifts across different interaction types; and 3) a simplified focal loss function that emphasizes hard-to-predict instances, improving robustness under data sparsity and behavioral ambiguity. The empirical validation is exceptionally strong, demonstrating improving of 22.43% on Tmall and 18.10% on Beibei over state-of-the-art baselines in HR@10 metrics, with particularly remarkable performance under sparse data conditions where traditional methods struggle most. Comprehensive ablation studies confirm that each component contributes meaningfully to the overall performance, while extensive hyperparameter analyses demonstrate the framework’s stability and practical applicability.