The traditional recommendation model based on knowledge graph generally adopts TransH strategy to represent the relations among nodes in the graph, and uses the interactive mode based on feature machine to learn recommendation. This method is not accurate enough to represent the relation among nodes, and often ignores the potential relations among nodes in the low dimensional data. In order to improve the accuracy of recommendation, this research proposed a new representation method based on polarization relation representation, which maps the representation among nodes to unitary space and enriches the effective information of the relations among nodes. In addition, an association learning method for knowledge graph embedding and low dimensional data of recommendation process was designed to deeply mine the rich and detailed relation hided in it, so as to improve the accuracy of recommendation. The experimental results show that the proposed method is effective. Compared with the results of the advanced methods in related fields, Recall Rate and Normalized Discounted Cumulative Gain（NDCG） have significant improvement on Amazon-book, Last-FM datasets.