In Ethiopia, there are several nations and ethnic groups which have their own language, culture, and custom. They play their traditional music and perform their own dance style, while traditional music is recorded as a video and uploaded on the internet for further use. But accessing traditional music videos from the internet is tiresome for the user. Therefore, to get those uploaded music videos easily from the internet it is required to develop a video classification model. From this perspective, this study aims to develop a classification model for Ethiopian traditional music videos to overcome the problem of inconveniency.To build this model we follow experimental research methods,for implementing an algorithm and empirically evaluating the efficiency and effectiveness and a purposive sampling technique is employed, because to collect the typical traditional music video that describes all the nations included in the study. From the selected 8 nations and ethnic groups 80 video clips were collected as a sample from different online video sharing platforms, and we get 10248 sequences of images for a dataset.Convolutional Neural Network (CNN),Optical Flow and Histogram Orientation Gradient (HOG) are used for feature extraction and also Convolutional Neural Network (CNN) is used as a classifier with python open-source tool.Finally the system has been tested using sequence of images, which were collected for testing and training purpose. Experimental results show that, when we use only CNN end to end classifier, the classification accuracy is 83% and when we use the combination of HOG, Optical flow with CNN features the classifier achieves good classification accuracy 86%. Key words: HOG, CNN, Optical Flow, Feature extraction, classifier