The existing unsupervised key phrase extraction models have insufficient capability to capture complex contexts and multi-level semantic information， thereby failing to acquire multi-dimensional information. Therefore， a key phrase extraction model based on multi-perspective information enhancement and hierarchical weighting was proposed. Firstly， the BERT （Bidirectional Encoder Representations from Transformers） pre-trained model was employed to encode the text and candidate phrases， thereby obtaining the embedding representations. Besides， the text embeddings were optimized through weighted average pooling， and the global similarity between them and candidate phrases was calculated to achieve global information enhancement， thereby improving the understanding of semantic associations. Secondly， a graph structure-based boundary-aware local centrality calculation method was introduced to improve the ability to capture local information. Finally， multiple factors were integrated for weight calculation to evaluate the importance of candidate phrases from various dimensions. Experiments were conducted on six public datasets such as Inspec， SemEval 2017， and SemEval-2010. The results show that compared to the baseline model PromptRank， the proposed model achieves improvements in F1@5 score by 0.87 to 2.68 percentage points， has the F1@10 score increased by 1.11 to 2.24 percentage points， and the F1@15 score improved by 0.54 to 2.25 percentage points. It can be seen that the overall performance of the proposed model has been enhanced effectively.