Ensuring the absence of vulnerabilities or flaws in smart contracts before their deployment is crucial for the smooth progress of subsequent work. Existing detection methods heavily rely on expert rules, resulting in low robustness and accuracy. Therefore, we propose EDSCVD, an enhanced deep learning vulnerability detection model based on dual-channel networks. Firstly, the contract fragments are preprocessed by BERT into the required word embeddings. Next, we utilized adversarial training FGM to the word embeddings to generate perturbations, thereby producing symmetric adversarial samples and enhancing the robustness of the model. Then, the dual-channel model combining BiLSTM and CNN is utilized for feature training to obtain more comprehensive and symmetric information on temporal and local contract features.Finally, the combined output features are passed through a classifier to classify and detect contract vulnerabilities. Experimental results show that our EDSCVD exhibits excellent detection performance in the detection of classical reentrancy vulnerabilities, timestamp dependencies, and integer overflow vulnerabilities.