Domain adaptation mainly deals with similar task decision across different data distributions. As an emerging branch of machine learning， domain adaptation has received much attention. With the rise of deep learning in recent years， the deep domain adaptation paradigm， as a combination of deep learning and traditional domain adaptation， has attracted more and more research. Although a variety of deep domain adaptation methods have been proposed， few systematic reviews have been published. To this end， this paper definitely reviews and analyzes the existing deep domain adaptation work and summarizes them to provide reference for relevant researchers. In conclusion， the main contributions of this work include the following aspects. Firstly， the background， concepts and application fields of domain adaptation are summarized. Secondly， according to whether the model training involves adversarial mechanism， we group the existing deep domain adaptation methods into two categories， such as deep adversarial domain adaptation and deep non-adversarial domain adaptation， and review and analyze them， respectively. Then， the benchmark datasets commonly used in the domain adaptation research are tabulated with profiles. Finally， the issues suffered in the existing deep domain adaptation work are summarized and analyzed， and future research directions are given.