Most work of Fine-Grained Entity Typing （FGET） focuses on how the semantic information of mention and context can be better coded， while ignoring the dependency between labels in the label hierarchy and their semantic information. In order to solve the above problem， a Hierarchy-Aware Fine-Grained Entity Typing （HAFGET） method was proposed. Firstly， the hierarchical encoder based on Graph Convolutional Network （GCN） was used to model the dependency between labels in different levels. Hierarchy-Aware Fine-Grained Entity Typing Multi-Label Attention （HAFGET-MLA） model and Hierarchy-Aware Fine-Grained Entity Typing Mention Feature Propagation （HAFGET-MFP） model were proposed. Then， HAFGET-MLA model and HAFGET-MFP model were carried out by using multi-label attention model and mention feature propagation model. In the former， the hierarchical perceptual label embedded was learned through the hierarchical encoder and the labels were classified after attention fusion with the mention features. In the latter， the mention features were directly input into the hierarchical encoder to update the feature representation and then classified. Experimental results on three public datasets FIGER， OntoNotes and KNET show that the accuracy and macro F1 scores of HAFGET-MLA model and HAFGET-MFP model are both improved by more than 2% compared with those of the baseline model. It is verified that the proposed method can effectively improve the typing effect.