Heterogeneous graphs, which can effectively capture the complex and diverse relationships between entities in the real world, play a crucial role in many domains. Heterogeneous graph representation learning aims to map the information in graphs into a low-dimensional space, so as to capture the deep semantic associations between nodes and support downstream tasks such as node classification and clustering. This study presents a comprehensive review of the latest research progress in heterogeneous graph representation learning, covering both methodological advancements and real-world applications. It first formally defines the concept of heterogeneous graphs and discusses the key challenges in heterogeneous graph representation learning. From the perspectives of shallow models and deep models. It then systematically reviews the mainstream methods for heterogeneous graph representation learning, with a particular focus on deep models. Especially for deep models, they are categorized and analyzed from the perspective of heterogeneous graph transformation. The strengths, limitations, and application scenarios of various methods are thoroughly analyzed, aiming to provide readers with a holistic research perspective. Furthermore, the commonly used datasets and tools in the field of heterogeneous graph representation learning are introduced, and their applications in the real world are discussed. Finally, the main contributions of this study are summarized and the outlook on the future research directions in this area is presented. This study intends to offer researchers a comprehensive understanding of the field of heterogeneous graph representation learning, laying a solid foundation for future research and application.