A fault detection algorithm for detecting serial arc faults in aviation communication lines has been proposed based on time-frequency domain fusion and the inclusion of efficient channel attention (ECA) in a one-dimensional convolutional neural network (1DCNN). Firstly, an aviation AC arc fault experimental platform was established, and loads of various types and parameter values were selected for the collection of current signals. Secondly, in order to retain more fault information, the time-domain signal was subject to a fast Fourier transform to observe the frequency spectrum, analyze its characteristic frequency bands, and after a large amount of data validation, it was found that when aviation serial arcing occurs, the 1000~4 000 Hz components have a certain proportion. Therefore, the original signal was fused with the characteristic frequency band, and the fused one-dimensional data was used as the input to the model. Finally, an ECA-1DCNN detection model was constructed and trained, and the validity of the model was verified through K-fold cross-validation to obtain an average accuracy of 97.96% on the test set. The method has a low number of network layers, quick computation, avoids complex time-frequency domain calculation processes, and is more intelligent, providing theoretical reference for the development of aviation serial arc detection devices.