The Android system has always dominated the mobile operating system. Its unique event-driven model and multi-threaded model also cause concurrency defects while enhancing the user experience and improving the program performance. In concurrent programs, the non-determinism of thread scheduling and the complexity of its reproducibility are the reasons for the difficulty of concurrency bug detection. The existing technologies mainly focus on the analysis of happens-before relationships based on the dynamic analysis, and then detect the concurrency bugs of Andriod applications (App for short). Nevertheless, there are still some problems of low coverage and high false positive (FP) due to the shortage of dynamic method. In this study, data races in Android applications are detected by the shared variable analysis and the constraint solving method, and detection tool, namely RaceDetector, is implemented. The tool firstly extracts the relevant information according to the characteristics of Android system and the definition of data race, and further expands the shared variable analysis to improve the accuracy and performance, and then it obtains a suspicious data race set with suspicious data race analyzing. Next, it identifies the feasible implementation of the path and the order of happens-before relationships according to every suspicious data race candidate through the method of constraint solving and finally detects the real data races. In experimental part, 15 popular applications with APK files are collected from Google Play and other sourcesas data sets. RaceDetector reports 340 data races on average, include 13% (44/340) of FP. Compared to existing tool, EventRacer, which triggers data races with 300 random events and reports 2 harmful data races on average, RaceDetector covers all thread schedules and event schedules, and it reports 15 harmful data races on average.