The invention discloses a network violent video identification method based on multiple examples and multiple characteristics. The method for identifying the network violent videos comprises the steps of grasping violent videos, non-violent videos, comments on the violent videos, comments on the non-violent videos, brief introductions of the violent videos and brief inductions of the non-violent videos from a 
video sharing network, and structuring a video data 
training set; extracting textural characteristics from textural information of the 
training set, forming textural characteristic vectors to 
train a textural pre-classifier, and screening out candidate violent videos by using the pre-classifier; using a shot segmentation 
algorithm based on a self-adapting 
dual threshold for conducting segmentation on video segments of the candidate violent videos, extracting related visual characteristics and 
voice frequency characteristics of each scene to express the scene, taking each scene as an example of multi-example study, and taking video segments as a 
package; and using an MILES 
algorithm for converting the 
package into a single example, using a characteristic vector for training a classifier model, and using the classifier model for conducting classification on the candidate violent videos. By the utilization of the network violence video identification method, bad influences that the network violent videos are broadcasted without constrain are largely lightened.