Aiming at problems of data sparseness and cold start in traditional Collaborative Filtering （CF） and problem that meta-path and random walk algorithms do not fully utilize node information， a Citation Recommendation Algorithm Fusing Knowledge Graph and Graph Attention Network （C-KGAT） was proposed. Firstly， knowledge graph information was mapped into low-dimensional dense vectors by using TransR algorithm to obtain embedded feature representation of the nodes. Secondly， through multi-channel fusion mechanism， graph attention network was used to aggregate neighbor node information to enrich semantics of target nodes and capture high-order connectivity between nodes. Thirdly， without affecting depth or width of network， dynamic convolutional layer was introduced to aggregate information of neighbor nodes dynamically to improve expression ability of the model. Finally， the interaction probabilities of users and citations were calculated through the prediction layer. Experimental results on public datasets AAN （ACL Anthology Network） and DataBase systems and Logic Programming （DBLP） show that the proposed algorithm performs better than all comparison models. The MRR （Mean Reciprocal Rank） of the proposed algorithm is increased by 6.0 and 3.4 percentage points respectively compared with that of the suboptimal model NNSelect， and the \mathrm{P} \mathrm{r} \mathrm{e} \mathrm{c} \mathrm{i} \mathrm{s} \mathrm{i} \mathrm{o} \mathrm{n} and \mathrm{R} \mathrm{e} \mathrm{c} \mathrm{a} \mathrm{l} \mathrm{l} indicators of the proposed algorithm also have different degrees of improvement， which verifies the effectiveness of the algorithm.