Many real-world networks can be treated as heterogeneous information networks (HINs) that consist of various types of nodes, like different proteins and molecules in biological networks and different authors and papers in citation networks. Multiple network data mining tasks can be conducted on HINs to capture the complex relationships between multi-type nodes. In recent years, random walk based HIN embedding has drawn increasing attention. Furthermore, the meta-path or meta-graph guided random walk is one of the most widely used techniques in HIN embedding methods. However, existing HIN embedding methods still face several difficulties. Firstly, the meta-paths or meta-graphs often need to be predefined, which relies heavily on domain knowledge and incomplete information coverage. Secondly, these methods treat all relations without distinction, which inevitably limits the capability of HIN embedding. Thirdly, they do not focus on preserving finer-grained meta-graph semantics. In this paper, a HIN embedding algorithm based on adaptive meta-schema considering relation distinction and semantic preservation (HINEAS) is proposed. In order to avoid the selection of meta-paths or meta-graphs, an adaptive meta-schema extraction is designed. In heterogeneous node sequence generation, a biased random walk strategy based on the adaptive meta-schema is presented to embed the different relationships’ influence. Finally, an enhanced embedding strategy based on semantic preservation of the adaptive meta-schema is proposed to effectively extract topology and preserve the meta-graph’s fine-grained semantics. Experiments on real-world datasets show that HINEAS significantly outperforms state-of-the-art methods.