In this paper, we present a novel feature encoding approach called Approximate Sparse Coding (ASC). ASC computes the sparse codes for a large collection of prototype descriptors in the off-line learning phase with Sparse Coding (SC); and look up the nearest prototype's sparse code for each to-be-encoded descriptor in the encoding phase with Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the fast speed of ANN, which are both desired properties for the human action recognition task. We excessively evaluated ASC on the popular HMDB51 dataset, and confirme it is able to encode large number of video features into discriminative low dimensional representations efficiently.