GitHub is a well-known open-source software development community that supports developers using the issue tracking system in each open-source project on GitHub to address issues. During the discussion of an issue about a defect, the developer may point out issues from other projects correlated to the defect, which are called cross-project issues, so as to provide reference information for fixing the defect. However, there are more than 200 million open-source projects and 1.2 billion issues on the GitHub platform, making it time-consuming to identify and acquire cross-project issues manually. This study presents a cross-project issue recommendation method CPIRecom for open-source software defects. This study builds a pre-selection set by filtering issues based on the number of historical issue pairs and the time interval for reporting issues. Then, the study also proposes an accurate recommendation model, which extracts textual features based on the pre-trained model of BERT, analyzes features of projects, calculates the relevant probability between defects and issues from the pre-selection set based on a random forest classifier, and obtains the recommendation list according to the ranking. This study simulates the application of CPIRecom method on GitHub platform. The mean reciprocal rank of CPIRecom method reaches 0.603, and the Recall@5 reaches 0.715 on the simulative test set.