Aiming at the problems of false positive and false negatives in data race detection, a novel static data race detection approach was proposed. Firstly, intra-thread and inter-thread function call graphs were automatically constructed via control flow analysis. Secondly, the information of variable-access events within thread were collected, and possible races were detected based on the defined data race conditions. Then, in order to improve the detection accuracy, alias variables and alias locks were analyzed to reduce false negatives and false positives, respectively. Finally, the sequential relationship between access events was abstracted through control flow analysis, and program slicing was used to determine the happens-before relationship of access events, thereby reducing false positives caused by ignoring thread interactions. A data race detection tool was implemented by Java and Soot framework based on this approach. In the experimentation, several benchmarks from JGF and IBM Contest benchmark suites, such as raytracer and airline, were selected for evaluation, and the results were compared with existing data race detection algorithm and tool (HB (Happens-Before) and RVPredict). The experimental results show that, compared with algorithm HB and tool RVPredict, total number of data races detected by the proposed approach are increased by 81% and 16% respectively, the accuracy of this approach for data race detection are respectively increased by 14% and 19%, which effectively avoids false negatives and false positives.