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Few-shot text classification targets at the situation where a model is developed to classify newly incoming query instances after acquiring knowledge from a few support instances.In this paper,we investigate few-shot text classification under a metric-based meta-learning framework.While the representations of the query and support instances are the key to the classification,existing study handles them independently in the text encoding stage.To better describe the classification features,we propose to exploit their interaction with adapted bi-directional attention mechanism.Moreover,distinct from previous approaches that encode different classes individually,we leverage the underlying cross-class knowledge for classification.To this end,we conceive the learning target by incorporating the large margin loss,which is expected to shorten the intra-class distances while enlarging the inter-class distances.To validate the design,we conduct extensive experiments on three datasets,and the experimental results demonstrate that our solution outperforms its state-of-the-art competitors.Detailed analyses also reveal that the bi-directional attention and the cross-class knowledge both contribute to the overall performance.