The invention discloses a coarse-to-fine video target behavior identification method, and the method comprises the steps: firstly obtaining human body key points by using an attitude estimation algorithm or annotation information in a video, and cutting and zooming different body parts of a human body; taking the deep neural network as a feature extraction network, and extracting feature vectors of different part areas; and iteratively training a classifier by utilizing the extracted feature vectors of different parts, and searching for the optimal coarse classification of the behavior; for the coarse classifier and each fine-grained classifier, selecting different parts to be cascaded with the global feature vector; carrying out individual training of the classifiers; and carrying out probability fusion on classification results of the coarse-grained classifier and the fine-grained classifier to obtain a final behavior recognition result. According to the method, a behavior recognition framework from coarse to fine is constructed, cascading is utilized to train classifiers in a targeted mode for feature expressions of different body parts of a person with different granularities,and therefore the probability of wrongly dividing similar behaviors is effectively reduced, and the overall behavior recognition accuracy is improved.