This paper describes our system used in theSemEval-2025 Task 9 The Food Hazard Detec-tion Challenge. Through data processing thatremoves elements and shared multi-task archi-tecture improve the performance of detection.Without complex architectural modificationsthe proposed method achieves competitive per-formance with 0.7835 Marco F1-score on sub-task 1 and 0.4712 Marco F1-score on sub-task2. Comparative experiments reveal that jointprediction outperforms separate task trainingby 1.3% F1-score, showing the effectiveness ofmulti-task learning of this challenge