In the field of deep learning， a large number of correctly labeled samples are essential for model training. However， in practical applications， labeling data requires high labeling cost. At the same time， the quality of labeled samples is affected by subjective factors or tool and technology of manual labeling， which inevitably introduces label noise in the annotation process. Therefore， existing training data available for practical applications is subject to a certain amount of label noise. How to effectively train training data with label noise has become a research hotspot. Aiming at label noise learning algorithms based on deep learning， firstly， the source， classification and impact of label noise learning strategies were elaborated； secondly， four label noise learning strategies based on data， loss function， model and training method were analyzed according to different elements of machine learning； then， a basic framework for learning label noise in various application scenarios was provided； finally， some optimization ideas were given， and challenges and future development directions of label noise learning algorithms were proposed.