Language prompting induces the model to produce a textual output during the training phase, which achieves remarkable performance in few-shot learning scenarios. However, current prompt-based methods either use the same task-specific prompts for each instance, losing the particularity of instance-dependent information, or generate an instance-dependent prompt for each instance, lacking shared information about the task. In this paper, we propose an efficient few-shot learning method to dynamically decide the degree to which task-specific and instance-dependent information are incorporated according to different task and instance characteristics, enriching the prompt with task-specific and instance-dependent information. Extensive experiments on a wide range of natural language understanding tasks demonstrate that our approach obtains significant improvements compared to prompt-based fine-tuning baselines in a few-shot setting with about 0.1% parameters tuned. Moreover, our approach outperforms existing state-of-the-art efficient few-shot learning methods on several natural language understanding tasks.