[Objective] This paper studies text classification task in few-shot learning setting and proposes a few-shot text classification model to address the issues of data scarcity and low generalization performance. [Methods] We divide the text classification task into multiple subtasks based on episode training mechanism in meta-learning. We propose a Bi-directional Temporal Convolutional Network(Bi-TCN) to capture the long-term contextual information of the text in each subtask. We propose Bi-directional Long-term Attention Network(BLAN) to capture more discriminative features based on Bi-TCN and multi-head attention mechanism. Neural Tensor Network is used to measure the correlation between query samples and support set of each subtask to achieve few-shot text classification. [Results] Experiments are performed on the ARSC dataset, the experimental results show that the classification accuracy of BLAN model reaches 86.80% in few-shot learning setting, which is improved by 3.68% and 1.17% respectively compared with the existing advanced few-shot text classification models ROBUSTTC-FSL and Induction-Network-Routing. [Limitations] The performance of BLAN on long text is not satisfactory. [Conclusions] BLAN overcomes the issue of data scarcity and captures comprehensive feature information of text, which effectively improves the performance of few-shot text classification.