Topic models have been successfully applied to information classification and retrieval. The difficulty in successfully applying these technologies is to select the appropriate number of topics for a given corpus. Selecting too few topics can result in information loss and topic omission, known as underfitting. Conversely, an excess of topics can introduce noise and complexity, resulting in overfitting. Therefore, this article considers the inter-class distance and proposes a new method to determine the number of topics based on clustering results, named average inter-class distance change rate (AICDR). AICDR employs the Ward’s method to calculate inter-class distances, then calculates the average inter-class distance for different numbers of topics, and determines the optimal number of topics based on the average distance change rate. Experiments show that the number of topics determined by AICDR is more in line with the true classification of datasets, with high inter-class distance and low inter-class similarity, avoiding the phenomenon of topic overlap. AICDR is a technique predicated on clustering results to select the optimal number of topics and has strong adaptability to various topic models.