Focusing on the issue that the existing action feature extraction method achieves lower recognition rate, a novel unsupervised one for action recognition by combining Dense Optical Flow trajectory and Sparse Coding (DOF-SC) algorithm was proposed. First of all, the trajectory-centered image patches were sampled as the original features based on the extraction of Dense Optical Flow (DOF). Then, the sparse dictionary on the basis of Sparse Coding (SC) framework was trained, and the sparse feature representation of the trajectory through dictionary was got, then the code book of the trajectory by clustering with the Bag-of-Feature (BF) model was achieved, and the trajectory of every action by code book was voted, the action features by counting the number of every code book were got. Finally, the examples for action recognition was classified and predicted by Support Vector Machine (SVM) with the kernel of histogram intersection function. The accuracy of the DOF-SC algorithm is superior to the accuracy of Motion Boundary Histogram (MBH) as the action feature by 0.9% in the KTH (Kungliga Tekniska Hgskolan) database and 1.2% in the YouTube database with the trajectory sampling rate of 10%. The results prove the effectiveness of the unsupervised action feature extraction method.