Prompt learning was proposed to solve the problem of inconsistency between the upstream and downstream tasks and has achieved State-Of-The-Art (SOTA) results in various Natural Language Processing (NLP) tasks. However, Relation Extraction (RE) is more complex than other text classification tasks, which makes it more difficult to design a suitable prompt template for each dataset manually. To solve this issue, we propose a Adaptive Prompt Construction method (APC) for relation extraction. Our method entails obtaining context-aware prompt tokens by extracting and generating trigger words associated with the entities. Furthermore, to alleviate the issue of instability in the prompt-tuning framework during training, we introduce a novel joint contrastive loss to optimize our model. Our method not only effectively reduces the human effort used for prompt template construction, but also achieves better performance in RE. We conduct the experiment on four public RE datasets, which demonstrate the proposed method outperforms the existing SOTA results in both datasets and experimental settings.