A knowledge-aware recommendation model called Fusion of Denoising Strategies and Multi-View Contrastive learning （FDSMVC）， was proposed to address the issues of poor noise reduction， inadequate extraction of semantic information between items， and imbalanced utilization of information in the Knowledge Graph （KG）-based recommendation models. Firstly， noise reduction was performed on the user-item interaction graph and the knowledge graph through dropping edges selectively and masking low-weight triplets with a weighted function， respectively. Secondly， random Singular Value Decomposition （SVD）， cosine similarity， k-Nearest Neighbors （kNN） sparsity， and path-based graph attention network were used to construct collaborative view， semantic view between items， and structural view， respectively. Thirdly， intra-graph， local， and global contrastive learnings were applied to multiple views. Finally， a multi-task strategy was applied to optimize the recommendation task and the contrastive learning task jointly， resulting in probability of user-item interactions. Experimental results show that on five real-world datasets： Book-Crossing， MovieLens-1M， Last.FM， Alibaba-iFashion， and Yelp2018， compared to the best baseline model， FDSMVC model achieves improvements of 1.06%-2.04% in Area Under the Curve （AUC） and 1.52%-2.06% in F1 score， and has the Recall@K also better than the best baseline model.