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 enhancement and Prompt Learning （KPL） method was proposed for Chinese few-shot news topic classification. Firstly， 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， simultaneously external knowledge was utilized to expand the label word space， enhancing the semantic richness of label words. Finally， predicted label words were subsequently mapped back to the original labels. Experiments were conducted on a few-shot training set and a few-shot validation set randomly sampled from three news datasets， THUCNews， SHNews and Toutiao. The experimental results show 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 method， the accuracy increases by at least 7.59， 2.11 and 3.10 percentage points， respectively， confirming the effectiveness of KPL in few-shot news topic classification tasks.