## 1 Introduction  
Networks in real life are mostly heterogeneous information networks containing various types of nodes and edges/relationships, which integrate more information than homogeneous networks. Because heterogeneous information network (shorted as HIN) embedding has a strong expressive ability and effectively combines node attribute characteristics with the characteristics of structural information, it can not only solve the problems of network data such as data sparse [1] but also has achieved remarkable improvements in various downstream tasks, such as node classification [2], link prediction [3], node aggregation Class [4] and recommendation [5]. Most of the existing network embedding studies focus on static HIN embedding [1, 6, 7, 8, 9], mainly considering how to effectively utilize the network topology and semantic information in dynamic heterogeneous information networks (shorted as DHINs) without considering any temporal information, while the network in the real world will change rapidly over time. The DHIN is a network that changes with time and contains the semantic features implied by the types of time features, nodes, and edges [10]. Take the citation network as an example; every minute, there will be new cooperation between scholars to produce new papers, which will constantly change the co-authorship and collection of papers, as shown in Figure 1. The existing static embedding methods cannot capture the structure brought by temporal information and semantic changes. Existing research has proven that ignoring the heterogeneous nature will result in a decrease in embedding quality due to information loss [8]. Since DHINs have temporal information on the basis of HINs, the edges of networks are usually generated between nodes that are semantically closely related but belong to different categories, and the edges between heterogeneous nodes that are semantically related also often have the same timestamp (for example, A\({}_{1}\)-P\({}_{1}\)-D\({}_{1}\), A\({}_{2}\)-P\({}_{2}\)-D\({}_{1}\) and A\({}_{2}\)-P\({}_{3}\)-D\({}_{2}\) are semantically closely related, but their categories are different). Different from the dynamic homogeneous network, affected by the semantic information of the DHIN, it is more likely that the semantically closely related nodes will have identical timestamps, rather than the occurrence time will have sequential and incremental timestamps (for example, the timestamps between A\({}_{1}\)-P\({}_{1}\)-D\({}_{1}\) are t\({}_{1}\); The timestamps between A\({}_{2}\)-P\({}_{2}\)-D\({}_{1}\) are t\({}_{2}\)), which will bring new challenges to the DHIN embedding. To sum up, the challenges of DHIN embedding mainly lie in how to make full use of the structural and semantic information in the network and how to make full use of the time information while considering the integrity of the network. In DHIN, the meta-paths contain rich semantic information, and a meta-path instance often corresponds to a specific event that occurs in the real world at a certain time, and it is of practical significance to explore the impact of meta path instances generated at different times on target nodes. Therefore, this paper proposes an embedding method for DHINs (shorted as MRDE). Based on the meta-path, the structure and semantic information of DHIN can be learned first for GCN based local embedding, then a temporal information processing mechanism is designed to fully consider the influence of time information with the help of time delay function and the attention mechanism is used to enhance the time influence of different meta-path instances on target nodes, and a meta-path level aggression module is used to learn more comprehensive node embedding. Node classification and node clustering experiments on real datasets verified the effectiveness of MRDE. The contributions of this paper are as follows:  
1. A DHIN embedding method based on meta-path and improved Rotate model (MRDE) is proposed, which can perform real-time and heterogeneous information learning;
2.