Most of the existing topic models are based on the Latent Dirichlet Allocation (LDA) or the variational autoencoder (VAE), but these methods have inherent flaws. The a priori assumptions of LDA on documents may not match the actual distribution of the data, and VAE suffers from information loss during the mapping and reconstruction process, which tends to affect the effectiveness of topic modeling. To this end, we propose a Prompt Topic Model (PTM) utilizing prompt learning for topic modeling, which circumvents the structural limitations of LDA and VAE, thereby overcoming the deficiencies of traditional topic models. Additionally, we develop a prompt word selection method that enhances PTM’s efficiency in performing the topic modeling task. Experimental results demonstrate that the PTM surpasses traditional topic models on three public datasets. Ablation experiments further validate that our proposed prompt word selection method enhances the PTM’s effectiveness in topic modeling.