Abstract: Gas identification is of great significance in the fields of environmental monitoring, industrial safety and medical health, which can effectively detect harmful gas leaks, monitor air quality and identify disease odor markers. However, the field of gas identification is faced with the problem that the sensor data needs to be processed manually before it can be used for subsequent analysis. Convolutional neural network (CNN) has been gradually applied in gas recognition scenarios of electronic nose systems with their ability of automatic feature learning and end-to-end modeling. Although CNN performs well in this field, there are still challenges such as limited receptive field and insufficient global feature extraction, resulting in limited recognition performance. To solve these problems, a gas recognition method based on expansive causal convolution and attention mechanism is proposed. The algorithm combines the attention mechanism and multi-scale temporal convolution network in Transformer to extract global and local features, extract more representational features and obtain a larger receptive field, and capture the instantaneous information and change trend of gas. Experiments are conducted on three different data sets—Open Sampling, Drift and Twin. The results show that the proposed method achieves accuracy of 99.47%, 99.61% and 99.22%, respectively, which are superior to the existing mainstream methods, thereby confirming its effectiveness.