With the aim to help developers better localize bugs, Researchers propose a series of text retrieval bug localization (TRBL) techniques. Such techniques take bug localization as an information retrieval task with a bug report being a query, all code elements being the document corpus, and the retrieved recommended documents being potential buggy code elements. Like any textual retrieval-based recommendation system, the success of TRBL techniques also largely depends on the quality of queries, i.e., bug reports. Knowing in advance whether a query would lead to relevant results (buggy code) is important for developers so that they can for example decide whether they should reformulate the query before wasting limited resources in checking irrelevant results. To this end, we propose an automatic query quality prediction approach for text retrieval-based bug localization. We take it as a typical classification task, by first collecting six categories of features evolving different aspects of bug reports and code, and then applying classical machine learning algorithms to build models to predict whether a bug report query would retrieve relevant buggy code. Through experiments on six projects, our approach could obtain an average accuracy of 72-91%, and F1 score of 71-91% over different TRBL techniques, and outperform existing techniques on average by 6-10.6% in accuracy, and 5.3-11.1% in F1 scores. We further explore the importance of different feature subsets and find several common features that contribute most to prediction performance.