Static analysis tools often suffer from high false positive rates of reported alarms, despite their ability to aid developers in detecting potential defects early in the software development life cycle. To improve the availability of these tools, many automated warning identification techniques have been proposed to assist developers in classifying false positive alarms. However, existing approaches mainly focus on using hand-engineered features or statement-level abstract syntax tree token sequences to represent the defective code, failing to capture semantics from the reported alarms. To overcome the limitations of traditional approaches, this study employs deep neural networks with powerful feature extraction and representation abilities to generate code semantics from control flow graph paths for warning identification. The control flow graph abstractly represents the execution process of a given program. Thus, the generated path sequences of the control flow graph can guide the deep neural networks to learn semantic information about the potential defect more accurately. In this study, the pre-trained language model is fine-tuned to encode the path sequences and capture the semantic representations for model building. Finally, the study conducts extensive experiments on eight open-source projects to verify the effectiveness of the proposed approach by comparing it with the state-of-the-art baselines.