Abstract: Key phrases encompass words or phrases pivotal in conveying essential information within an article, succinctly encapsulating its core themes and primary content. Key phrase extraction stands as a fundamental task within the realms of information retrieval and text search. Current mainstream methods for phrase extraction typically involve multiple stages, where the initial selection of candidate phrases significantly influences the outcome. Moreover, pre-trained language models, while potent, are not inherently tailored for the task of phrase extraction, and mere embedding comparisons may inadequately capture the correlation between phrases and documents. Addressing these challenges, an unsupervised key phrase extraction approach employing soft prompt tuning is proposed. Firstly, prefix vectors are introduced to model the noise and semantic information of words, followed by feature extraction through linear transformation of the pre-trained model output. Secondly, KL divergence is employed to amplify the disparity between the two types of information in words, while variance loss serves to mitigate model collapse. Finally, the importance of words is determined in a single step based on the degree of differentiation between the two types of information, yielding key phrases. Comparative evaluation against existing methods on the Inspec and SemEval2017 datasets demonstrates an average 1% increase in the F1 score.