To solve the drift problem in the electronic nose sensor array, a long-term drift suppression method with the augmented convolutional neural network is proposed. First, by combining historical data to expand the database, it has the effectiveness of data enhancement. Then, the incremental compensation module is used for network training to enhance the entire network performance. Finally, public dataset and measured dataset are utilized to evaluate the drift suppression performance, respectively. Compared with the traditional convolutional neural network and machine learning algorithms, experimental results show that the proposed augmented convolutional neural network ( ACNN) has great accuracy increase about 10% - 20% , and the accuracy fluctuation of 1% is good robustness, which verified that the augmented convolutional neural network is robust and effective in the suppression of electron nose drift, at the same time, also provides ideas for the drift suppression of electronic nose from the algorithm level.