Abstract: Classification methods based on fine-tuning pre-trained models usually require a large amount of annotated data, resulting in the inability to be used for few-shot classification tasks. Therefore, a Knowledge-Enhanced and Prompt Learning (KPL) method was proposed for Chinese few-shot news topic classification. Firstrly, an optimal prompt template was learned from the training set by using a pre-trained model. Then the template was integrated with the input text, effectively transforming the classification task into a cloze-filling task. External knowledge was simultaneously utilized to expand the label word space, enhancing the semantic richness of label words. Predicted label words were subsequently mapped back to the original labels. Experiments conducted by randomly sampling on three news datasets, THUCNews, SHNews and Toutiao, to form a small-sample training set and validation set. The experimental results shown that the proposed method improves the overall performance on the 1-shot, 5-shot, 10-shot, and 20-shot tasks on the above datasets.. Notably, a significant improvement is observed in the 1-shot task. Compared to baseline few-shot classification methods, the accuracy rate increased by 7.95%, 2.11%, and 3.1% respectively, confirming the effectiveness of the knowledge enhancement and prompt learning approach in few-shot news topic classification tasks.