With the rapid development of deep learning, research on deep learning frameworks and hardware has become a crucial direction for advancing the field. Frameworks provide developers with convenient tools for building deep learning models, while hardware delivers powerful computational capabilities. However, limited adaptability and insufficient compatibility between diverse deep learning frameworks and hardware platforms often lead to performance and scalability issues. To address this challenge, deep learning compilation technology has emerged. Models from different frameworks are efficiently mapped to executable code for specific backend devices through a series of intermediate representations and automated transformations. During this process, various deep learning compilation optimization techniques, such as operator fusion, memory optimization, and auto-tuning, are applied. These not only resolve scalability issues but also significantly improve the computational efficiency of the models. This study first reviews the basic concepts and overall process of deep learning compilation, then summarizes and categorizes the common optimization techniques in deep learning compilation, and finally discusses the challenges faced by the field and potential future development directions.