Aiming at the problem that the training samples for few-shot learning are too few， which leads to the weak expression of features， a novel dynamic routing prototypical network based on SLDA（DRP-SLDA） is proposed based on the supervised topic model（Supervised LDA， SLDA） and dynamic routing algorithm. The SLDA topic model is used to establish the semantic mapping between words and categories， enhance the category distribution characteristics of words， and obtain the semantic representation of samples from the perspective of word granularity. The dynamic routing prototypical network（DR-Proto） is presented. The network makes full use of the semantic relationship between samples by extracting cross features， and uses the dynamic routing algorithm to iteratively generate dynamic prototype with category representation， so as to solve the problem of feature expression. The experimental results show that the DRP-SLDA model can effectively extract the category distribution characteristics of words and dynamically obtain the dynamic prototype to increase the category identification， which can obviously improve the generalization ability of few-shot text classification.