Although knowledge graph have shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contextual features, i.e., feature sparsity and interaction sparsity. To bridge this gap, in this paper, we propose a novel pairwise intent graph embedding learning (PING) framework to efficiently integrate knowledge graph into CARS. Specifically, our PING contains three modules: 1) a graph construction module is used to obtain a pairwise intent graph (PIG) containing nodes for users, items, entities and enhanced intent, where enhanced intent nodes are generated by applying user intent fusion (UIF) on relational intent and contextual intent, and two sub-intents are derived from the semantic information and contextual information, respectively; 2) a pairwise intent joint graph convolution module is used to obtain the refined embeddings of all the features by executing a customized convolution strategy on PIG, where each enhanced intent node acts as a hub to efficiently propagate information among different features and between all the features and knowledge graph; 3) a recommendation module with the refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our PING.