With the widespread application of deep learning， human beings are increasingly relying on a large number of complex systems that adopt deep learning techniques. However， the black?box property of deep learning models offers challenges to the use of these models in mission?critical applications and raises ethical and legal concerns. Therefore， making deep learning models interpretable is the first problem to be solved to make them trustworthy. As a result， researches in the field of interpretable artificial intelligence have emerged. These researches mainly focus on explaining model decisions or behaviors explicitly to human observers. A review of interpretability for deep learning was performed to build a good foundation for further in?depth research and establishment of more efficient and interpretable deep learning models. Firstly， the interpretability of deep learning was outlined， the requirements and definitions of interpretability research were clarified. Then， several typical models and algorithms of interpretability research were introduced from the three aspects of explaining the logic rules， decision attribution and internal structure representation of deep learning models. In addition， three common methods for constructing intrinsically interpretable models were pointed out. Finally， the four evaluation indicators of fidelity， accuracy， robustness and comprehensibility were introduced briefly， and the possible future development directions of deep learning interpretability were discussed.