Service description texts for Internet of Things （IoT） are short in length and sparse in text features， and direct modeling the IoT service by using traditional topic model has poor clustering effect， so that the best service cannot be discovered. To solve this problem， an IoT service discovery method based on Biterm Topic Model （BTM） was proposed. Firstly， BTM was employed to mine the latent topic of the existing IoT services， and the service document-topic probability distribution was calculated and deduced through global topic distribution and theme-word distribution. Then， K-means algorithm was used to cluster the services and return the best matching results of service requests. Experimental results show that the proposed method can improve the clustering effect of services for IoT and thus obtain the matched best service. Compared with the methods of HDP （Hierarchical Dirichlet Process） and LDA-K （Latent Dirichlet Allocation based on K-means）， the proposed method achieves better performance in terms of Precision and Normalized Discounted Cumulative Gain （NDCG） for best service discovery.