Current deep learning models for side-channel analysis are often parameter-inefficient and lack robustness in feature extraction under noisy conditions, thereby requiring a substantial number of power traces to recover the key. We propose Stal-net, a lightweight task-oriented architecture for profiling side-channel analysis. It integrates lightweight local feature extraction, temporal dependency modeling, dynamic-threshold-based feature refinement, and correlation-aware multi-scale fusion in a coordinated pipeline, improving feature discriminability while controlling model complexity. Experiments were conducted on the ASCAD and DPA contest v4 datasets. Key recovery performance is evaluated using classification accuracy and average key rank, and attack efficiency is measured by the average number of power traces required to achieve average key rank of 0. On ASCAD, Stal-net achieves 94.23% accuracy and reaches average key rank = 0 with only 22 power traces, improving accuracy by 1.33% and attack efficiency by 26.7% over the baseline. On DPA contest v4, Stal-net achieves 93.4% accuracy and reaches average key rank = 0 with only 2 power traces, improving accuracy by 5.18% and attack efficiency by 33.3%. Ablation studies further demonstrate the complementary roles of the four core modules in noise suppression, temporal modeling, and feature fusion. This collaborative mechanism is the key enabler for Stal-net to maintain high performance while adhering to a lightweight design.