Aspect-based sentiment analysis (ABSA) is an NLP task that classify fine-grained sentiment towards one specific aspect from the same text. While Pretrained language models (PLMs) like BERT have been widely researched in ABSA, attaching aspects to corresponding sentiment remains challenging. In this paper, we propose Multi-task Multi-prompt learning Model (3M) to introduce more proper semantic information for different task aims. In 3M, the ABSA task is divided into two subtasks, a classification task for aspect categorization and a generation task for sentiment analysis. For specific task, we design respective prompt learning approach to help model understand ABSA more accurately. Note that the overfitting using BERT on ABSA, we utilize the method of float loss to restrict the training not too small. The experiment result shows that 3M achieves results comparable to SOTA.