Aiming at the problem that qualifiers in the hyper-relational knowledge graph will introduce irrelevant noise into the main triple， a Hyper-Relational knowledge graph completion method fusing Noise Filtering （HRNF） was proposed. Firstly， a feature enhancement module was constructed in order to enhance the hyper-relational facts effectively. At the same time， Convolutional Neural Network （CNN） was utilized to extract the ordinary triple features， and complex relational features in the hyper-relational fact were captured by Heterogeneous Graph Neural Network （HGNN）. Secondly， these two features were fused to enhance information of the main triple in the hyper-relational fact by utilizing stability and reliability of the ordinary triple， so as to reduce the effect of noise introduced by qualifiers. Thirdly， a relevance-aware module was constructed to fuse the feature representations more accurately. At the same time， Graph ATtention network version Two（GATv2） was utilized to update the enhanced feature representation by learning weights among different nodes dynamically. Fourthly， a semantic enhancement module was constructed to capture complex semantic information. Finally， Transformer model was utilized to generate the final predicted sequence by capturing the dependency between any two elements in the sequence through self-attention mechanism. To validate the effectiveness of HRNF， extensive experiments were conducted on two commonly used datasets， Wikipeople and JF17K. The results show that when predicting main triple entities， compared to the optimal GRAN （GRAph-based N-ary relational learning） of the baseline methods， the Mean Reciprocal Rank （MRR）， Hits@1， and Hits@10 of HRNF are improved by 0.6， 1.1， and 1.8 percentage points， respectively， on Wikipeople dataset， and the MRR， Hits@1， and Hits@10 of HRNF are improved by 0.5， 0.7， and 2.9 percentage points， respectively， on JF17K dataset. The above significant improvements prove that in dealing with task of hyper-relational knowledge graph completion， HRNF can reduce the noise problem brought by qualifiers effectively.