The invention discloses a few-time text classification method under a meta-learning framework based on measurement, which comprises the following steps: in an input layer, inputting a support instanceand a query instance; in a word embedding layer, mapping discrete words into a vector space by searching a pre-trained word embedding table; optimizing, at a context encoder layer, a local representation of each word in sentences supporting instances and query instances by considering contexts; in the bidirectional attention layer, firstly coupling a query instance with each support instance, andthen generating matching information between the query instance and each support instance; in the model layer, forming feature vectors for the query instances and the support instances, and calculating weights of the support instances by a given query instance-level attention module to dynamically generate a prototype; at an output layer, providing prediction for query instances by measuring similarity scores between queries and prototypes. According to the method, a few-time text classification framework using a bidirectional attention mechanism and cross-class knowledge is provided, so thatthe few-time text classification method is more effective.