The existing recommendation method with knowledge graph and privacy protection cannot effectively balance the noise of Differential Privacy （DP） and the performance of recommender system. In order to solve the problem， a News Recommendation method with Knowledge Graph and Privacy protection （KGPNRec） was proposed. Firstly， the multi-channel Knowledge-aware Convolutional Neural Network （KCNN） model was adopted to merge the multi-dimensional feature vectors of news title， entities and entity contexts of knowledge graph to improve the accuracy of recommendation. Secondly， based on the attention mechanism， the noise with different magnitudes was added in the feature vectors according to different sensitivities to reduce the impact of noise on data analysis. Then， the uniform Laplace noise was added to weighted user feature vectors to ensure the security of user data. Finally， the experimental analysis was conducted on real news datasets. Experimental results show that， compared with the baseline methods such as Privacy-Preserving Multi-Task recommendation Framework （PPMTF） and recommendation method based on Deep Knowledge-aware Network （DKN）， the proposed KGPNRec can protect user privacy and ensure the prediction performance of method. For example， on the Bing News dataset， the Area Under Curve （AUC） value， accuracy and F1-score of the proposed method are improved by 0.019， 0.034 and 0.034 respectively compared with those of PPMTF.