In order to overcome the long learning time caused by searching optimal basic function data based on greedy strategy from a redundant basis function dictionary for the Intuitionistic Fuzzy Kernel Matching Pursuit (IFKMP), the random Intuitionistic Fuzzy Kernel Matching Pursuit algorithm based on weak greedy strategy is proposed. Rather than getting the present optimal basic function in each search, the approximate optimal basic function can be obtained by searching a random kernel dictionary subset of original searching space. So the searching space of matching pursuit can be reduced, and the training time can be decreased greatly. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously leaving the classification accuracy almost unchanged, while the model has better sparsity and generalization.