Predicting drug-target interactions by bioinformatics method is efficient for understanding pharmacological effects and advancing biomedical research. A number of structure-based, ligand-based and network-based approaches have emerged. Furthermore, the integration of graph attention networks with intricate drug-target studies is an application area of growing interest. Here, we formulate a model called MKDTI by extracting kernel information from various layer embeddings of a graph attention network. This combination improves the prediction ability with respect to novel drug-target relationships. We first build a drug-target heterogeneous network using heterogeneous data of drugs and targets. Then a self-enhanced multi-head graph attention network is used to extract potential features in each layer. Next, we utilize embeddings of each layer to extract kernel matrices and fuse multiple kernel matrices. Finally, we use a dual Laplacian regularized least squares framework to predict novel drug-target entity connections. Compared to the benchmark algorithms, our model outperforms them in the prediction outcomes. In addition, we conduct an experiment on kernel selection. The results show that the multi-kernel fusion approach combined with the kernel matrix generated by the graph attention network provides complementary insights into the model. The fusion of this information helps to enhance the accuracy of the predictions.