Identification of ignitable liquids (ILs) plays an important role in fire investigation. Electronic noses (E-noses) have been used in the field of ILs identification due to their advantages of fast speed, low price and good portability. However, when we ran the same algorithm on different E-noses with the same sensor array (i.e. the same type and number of gas sensors) to identify the same ILs, we found that the identification results were different, mainly because of the poor consistency of the gas sensors used in the E-noses. To make the identification results of all E-noses essentially the same, each E-nose must be trained with a large amount of data individually, which would waste a lot of time. To address the issue of ILs identification across E-noses, we proposed a multi-branch adaptive feature fusion network (MAFF-Net) that can simultaneously process one-dimensional (1D) data directly output from E-noses and two-dimensional data converted from 1D data based on Gramian angular field. The MAFF-Net can be trained using one E-nose and then transplanted to another E-nose by fine-tuning its model parameters with a small training dataset. To verify the performance of the proposed MAFF-Net, three E-noses were designed and fabricated, and an ILs dataset was constructed based on the self-built E-noses. The experimental results show that the recognition accuracy of the MAFF-Net algorithm on E-nose No.1, No.2 and No.3 is 100.0%, 97.5% and 90% respectively, which is better than the five classical algorithms.