Stack Overflow is one of the most popular software question and answer communities, where users can post questions and receive answers from others. In order to ensure the quality of questions, the website needs to promptly discover and delete questions with low quality or not conforming to the community’s theme. Currently, Stack Overflow mainly relies on manual inspection to find questions that need to be deleted. However, this way usually hardly guarantees to discover and delete questions in time, and increases the burden of community administrators. In order to quickly find questions that need to be deleted, this study proposes a method to automatically predict question deletion, which is named MulPredictor. This method extracts the semantic content features, the semantic statistical features and the meta features of a question, and uses the random forest classifier to calculate the probability that it will be deleted. Experimental results showed that, compared with existing methods DelPredictor and NLPPredictor, MulPredictor increases the accuracy by 16.34% and 12.78% on balanced test set, and increases the accuracy by 12.38% and 14.14% on random test set. In addition, this study also analyzes important features in question deletion, and finds that the code segment, the question’s title, and the first paragraph of the question’s body have the most significant impacts on question deletion.