The existing event classification (EC) work primarily focuses on the traditional supervised learning setting in which models are unable to extract event mentions of new/unseen event types. Few-shot learning has not been investigated in this area although it enables EC models to extend their operation to unobserved event types. To fill in this gap, in this work, we investigate event classification under the few-shot learning setting. We propose a novel training method for this problem that extensively exploit the support set during the training process of a few-shot learning model. In particular, in addition to matching the query example with those in the support set for training, we seek to further match the examples within the support set themselves. This method provides more training signals for the models and can be applied to every metric-learning-based few-shot learning methods. Our extensive experiments on two benchmark EC datasets show that the proposed method can improve the best reported few-shot learning models by up to 10% on accuracy for event classification.