A biterm topic model with word vector and noise filtering (BTM-VN) was proposed, in order to mine high-quality latent topics, improve the accuracy of service clustering, and solve sparsity and noise problems caused by the short text feature of service description documents, Based on biterms, BTM-VN expanded the service description documents and obtained additional semantic information. A strategy for calculating the probability of representative biterms based on topic distribution information was designed. By calculating a representative biterms matrix in the sampling process, the weight of the representative biterms at the current topic was improved to reduce the interference of noise words in the service description document. Moreover, word embeddings were integrated to filter the biterms, reducing the number of biterms with low co-occurrence meaning and solving the biterm-based topic model’s problem which causes high time consumption. Finally, an optimized density peak clustering algorithm was used to cluster the topic distribution matrix trained by BTM-VN. Experimental results show that, the service clustering method based on BTM-VN performs better on real-world dataset than existing methods according to three clustering evaluation metrics.