Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments.