Abstract: The bag-of-words (BoW) model usually causes large errors and weak discrimination in video representation in video action recognition, and affects the human action recognition accuracy. To solve this problem, a discriminative sparse coding (DSC) video representation algorithm is proposed. It's a sparse coding framework involving a Fisher discriminative analysis to encode video local spatial-temporal features and increase the video sparse representation discrimination. And an online discriminative dictionary learning algorithm is also proposed to train a dictionary from massive video data. The experiments show that, comparing with the existing algorithms, the proposed algorithm effectively improves human action recognition accuracy.