Existing approaches for deep-learning-based data race detection are suffering from the issues of single feature extraction and low accuracy. To improve the state-of-the-art, a novel approach called DeleRace is proposed to detect data race based on deep learning model. Firstly, DeleRace extracts instruction-level, method-level, and file-level features from a variety of real-world applications based on static analysis tool WALA. All these features are transformed by word vectorization to build the training dataset. Secondly, ConRacer, as an existing data race tool, is employed to identify the real race. Based on this tool, those positive samples in the training dataset is labelled. To further optimize the dataset, DeleRace leverages SMOTE algorithm to distribute both positive samples and negative ones in balance. Finally, CNN-LSTM model is constructed and a classifier is trained to detect data race. In the experimentation, a total of 26 real-world applications is selected from different fields in DaCapo, JGF, IBM Contest and PJBench benchmark suites. The experimental results show that the accuracy of DeleRace is 96.79% which is 4.65% higher than existing deep-learning-based approaches. Furthermore, the performance of DeleRace is compared with that of both dynamic tools (such as Said and RVPredict) and static tools (such as SRD and ConRacer), which demonstrates the effectiveness of DeleRace.