Focusing on the issues that text feature extraction lacks consideration of the contextual discriminative features of sentences and fails to fully utilize the association information among instances and relation labels， a method combining Semantic enhancement and Perception attention for Relation Extraction （SPRE） was proposed. Firstly， during the sentence feature encoding phase， a Semantic Enhancement Mechanism （SEM） was constructed to extract salient semantic features of sentences， and a salient information enhanced sentence representation was obtained through entity-aware word embeddings and Salient Feature Perception （SFP）. Then， a Perception Attention Mechanism （PAM） was designed to integrate sentence features. In the mechanism， the matching degree among sentences and relation labels was evaluated by perceiving the semantic information among sentences and relation labels， the consistency information among entity types of sentences and the corresponding relations， and the similarity information among sentences， so as to fully utilize the dependencies between instances and relation labels in a bag， thereby further enhancing noise reduction capability of the method. Finally， after conducting relation prediction by a classifier， the network parameters were adjusted according to cross-entropy between the predicted results and the actual results. Experimental results on NYT-10 （New York Times 10） and GDS （Google Distant Supervision） datasets show that on NYT-10 dataset， compared with the BERT （Bidirectional Encoder Representations from Transformers）-based relation extraction method PARE （Passage-Attended Relation Extraction）， the proposed method achieves an Area Under Curve （AUC） increase of 2.1 percentage points and an average precision Precision@N （P@N） — P@M increase of 2.4 percentage points for the top 100， 200， and 300 data entries ranked in descending order of confidence； on GDS dataset， the AUC and P@M of the proposed method are 90.5% and 97.8% respectively. The proposed method outperforms mainstream distant supervision relation extraction methods on both datasets significantly， verifying the effectiveness of this method. It can be seen that in mainstream distant supervision relation extraction tasks， the proposed method can enhance the model’s ability to learn data features effectively.