Aiming at the problems that the existing knowledge graph recommendation models do not consider the periodic features of the user and the items to be recommended will affect the recent interests of the user， a knowledge graph recommendation model with Multiple Time scales and Feature Enhancement （MTFE） was proposed. Firstly， Long Short-Term Memory （LSTM） network was used to mine the user’s periodic features on different time scales and integrate them into user representation. Then， attention mechanism was used to mine the features strongly correlated with the user’s recent features in the items to be recommended and integrate them into the item representation after enhancement. Finally， the scoring function was used to calculate user’s ratings of items to be recommended. The proposed model was compared with PER（Personalized Entity Recommendation）， CKE（Collaborative Knowledge base Embedding）， LibFM， RippleNet， KGCN（Knowledge Graph Convolutional Network）， CKAN（Collaborative Knowledge-aware Attentive Network） knowledge graph recommendation models on real datasets Last.FM， MovieLens-1M and MovieLens-20M. Experimental results show that compared with the model with the best prediction performance， MTFE model has the F1 value improved by 0.78 percentage points， 1.63 percentage points and 1.92 percentage points and the Area Under Curve of ROC （AUC）metric improved by 3.94 percentage points， 2.73 percentage points and 1.15 percentage points on three datasets respectively. In summary， compared with comparative knowledge graph recommendation models， the proposed knowledge graph recommendation model has better recommendation effect.