Abstract: Topic modelling is an important text mining method in natural language processing, but its modeling process is complex and can generate results that do not always align with user expectations. To address this issue and enable non-expert users to understand the modelling process and modify model results quickly, we propose the interactive visual analysis system BTVis based on BERTopic. The system enhances the interpretability and usability of BERTopic through the following key features: 1) explored and visualized the intermediate process of hierarchical clustering of BERTopic to intuitively reveal the topic generation mechanism; 2) analyzed the outlier documents to uncover potential relationships between the outlier documents and the topics; 3) proposed the multi-granular local model editing algorithms to enhance the accuracy of the BERTopic model; 4) implemented the interactive hierarchical topic modeling system BTVis in a web environment, allowing users to enhance BERTopic results through visual analysis and interactive exploration. We conducted experiments including qualitative analysis, user experiments and quantitative tests with multiple real datasets. The results show the effectiveness and practicality of the system.