Abstract: Heterogeneous graph (HG) embedding method has been proposed as a new learning paradigm that embeds vertices into a low-dimensional dense vector space, by preserving Heterogeneous graph topology structure and vertex attributes information. In order to improve the quality of HG embedding and reduce distortion, a method for embedding HGs into hyperbolic space based on Lorentz model is proposed. This method employ the meta-path guided random walk to capture the structure and semantic relations between nodes. Specifically, the maximum likelihood estimate based on negative sampling is used as the objective function to achieve binary classification: making the target node more similar to its neighbor and farther away from non-neighbor nodes. Then, the Riemann gradient descent, which is different from the Euclidean space, is used to optimize the model parameters. Experiments on PubMed dataset demonstrate that our proposed model not only has superior performance on link prediction tasks than 4 baseline methods but also show its ability of capture graph’s hierarchy structure. Hyperbolic space provides a new idea for analyzing structure of heterogeneous graphs and can provide higher-quality embedding results for downstream tasks of heterogeneous graphs.