Objective：To evaluate the clinical value of deep learning（DL）- based reconstruction algorithm for half - Fourier acquisition single - shot turbo spin echo（HASTE）T2 - weighted imaging（T2WI）in pancreatic magnetic resonance imaging（MRI）. Methods：A total of 69 patients underwent pancreatic conventional BLADE-TSE-T2WI and based on deep learning HASTE-DL-T2WI sequences scanning using 3.0T MR. The overall image quality，pancreatic sharpness，biliary duct clarity，and artifacts were subjectively scored using a Likert 5-point scale. The contrast to noise ratio（CNR）and signal to noise ratio（SNR）of normal pancreatic tissue and the lesion in both sequences were measured and compared，and the scan time was recorded. Results：The HASTE - DL sequence scored significantly higher in overall image quality，pancreatic sharpness，and bile duct clarity than the BLADE - TSE sequence（P ＜ 0.001），with no statistical difference in artifact scores（P ＞ 0.05）. The SNR of normal pancreatic tissue，lesion SNR，and CNR in HASTE-DL images were superior to those of BLADE-TSE sequence（P ＜ 0.001）. Additionally，the scanning time of HASTEDL was reduced by 78% compared to BLADE -TSE. Conclusion：Compared to BLADE -TSE sequence，HASTE -DL provides better overall image quality，superior pancreatic sharpness and bile duct clarity，higher SNR and CNR，and significantly shorter scan time. Thus，HASTE-DL T2WI demonstrates excellent clinical utility in pancreatic MRI.