To address the problem of low efficiency for traditional massive image classification, a sparse representation based global dictionary learning method was designed. The traditional dictionary learning steps were distributed to parallel nodes, local dictionaries were first learnt in local nodes and then a global dictionary was updated in real time by those local dictionaries and variables through using convex optimization method, thereby enhancing the efficiency of dictionary learning and classification of massive data. Experiments on the MapReduce platform show that the new algorithm has better performance than classical image classification methods without affecting the classification accuracy, and the new algorithm can be widely used in massive and distributed image classification tasks.