Code completion is one of the crucial functions of automation software development. It is an essential component of most modern integrated development environments and source code editors. Code completion provides predictions such as instant class names, method names, keywords, and assists developer to code, which improves the efficiency of software development intuitively. In recent years, with the expanding of the source code and data scale in the open-source software community, and outstanding progress in artificial intelligence technology, the automation software development technology has been much promoted. Intelligent code completion builds a language model for source code, learns features from the existing code corpus, and retrieves the most similar matches in the corpus for recommendation and prediction based on the context code features around the position to be completed. Compared to traditional code completion, intelligence code completion has become one of the hot trends in the field of software engineering with its characteristics like high accuracy, multiple completion forms, and iterative learning ability. Researchers have conducted a series of researches on intelligent code completion. According to the different forms that these completion methods represent and utilize source code information, they can be divided into two research directions: programming language representation and statistical language representation. The programming language is divided into three types: token sequences, abstract syntax tree, and control/data flow graph. The statistical language also has two types: n-gram model and the neural network model. This paper starts from the perspective of code representation and summarizes the research progress of code completion methods in recent years. The main contents include: (1) expounding and classifying existing intelligent code completion methods according to code representation; (2) summarizing the experimental verification methods and performance evaluation indicators used in model evaluation; (3) summarizing the critical issues of intelligent code completion; (4) looking forward to the future development of intelligent code completion.