Data races are common defects in multi-threaded programs. Traditional data race analysis methods fail to achieve both recall and precision, and their detection reports cannot locate the root cause of defects. Due to the advantages of Petri nets in terms of accurate behavior description and rich analysis tools in the modeling and analysis of concurrent systems, this study proposes a new data race detection method based on Petri net unfolding technology. First, a Petri net model of the program is established by analyzing and mining a program running trace. The model implies different traces of the program even though it is mined from a single trace, which can reduce the false negative rate of traditional dynamic analysis methods while ensuring performance. After that, a Petri net unfolding-based detection method of program potential data races is proposed, which improves the efficiency significantly compared with static analysis methods and can clearly show the triggering path of data race defects. Finally, for the potential data race detected in the previous stage, a scheduling schema is designed to replay the defect based on the CalFuzzer platform, which can eliminate false positives and ensure the authenticity of detection results. In addition, the corresponding prototype system is developed, and the effectiveness of the proposed method is verified by open program instances.