The method, which combines Sentence-BERT and LDA, takes the topic number of LDA as the k value in K-means algorithm, resulting in poor interpretability and low topic consistency. To solve this problem, a Sentence-BERT and LDA optimization method based on density Canopy(SBERT-LDA-DC) was proposed, which used density Canopy to improve the K-means algorithm. The experimental results indicate that this method is superior to similar methods using K-means and K-means++ to cluster feature vectors on the consistency index. Compared with the SBERT-LDA method, the consistency index is improved by 229% on the 1 852 drama comment dataset. The proposed SBERT-LDA-DC method is effective, which provides a new method for product or service providers to better understand user opinions and improve their own products or services, and has strong practical application value.