Because the existing motion recognition method couldnot be directly used in human micro-expression action recognition. A spatio-temporal pyramid Weber Local Descriptor (STPWLD) was constructed and an automatic recognition algorithm of human lower limb micro-expression action based on dictionary learning according to characterize human lower limb micro-expression action was designed. With the method， the features of human lower limb micro-expression action was extracted by the STPWLD. Then， the dimensions of STPWLD feature were reduced by the principal component analysis. Furthermore， the sub-dictionaries of human lower limb micro-expressions action was established and these sub-dictionaries were connected in series to construct a general action dictionary. Finally， the influence of the parameters of the algorithm on the recognition results was analyzed， and the optimal value of these parameters was determined. It shows that the optimal value of pyramid scales is 3， the optimal feature dimension of each action after dimension reduction is 30， the optimal number of atoms in each action dictionary is 40 and the optimal value of sparse threshold is 10. The experimental results indicate that the recognition rates of the proposed algorithm for 10 kinds of human lower limb micro-expression actions are all between 0.83~0.91， and the average recognition rate is 0.86， higher than that of other algorithms. The algorithm is suitable for the classification of human lower limb micro-expression actions and improves the classification recognition rate effectively.