  The rapid development of new generation radio interferometers such as the
Square Kilometer Array (SKA) has opened up unprecedented opportunities for
astronomical research. However, anthropogenic Radio Frequency Interference
(RFI) from communication technologies and other human activities severely
affects the fidelity of observational data. It also significantly reduces the
sensitivity of the telescopes. We proposed a robust Convolutional Neural
Network (CNN) model to identify RFI based on machine learning methods. We
overlaid RFI on the simulation data of SKA1-LOW to construct three visibility
function datasets. One dataset was used for modeling, and the other two were
used for validating the model's usability. The experimental results show that
the Area Under the Curve (AUC) reaches 0.93, with satisfactory accuracy and
precision. We then further investigated the effectiveness of the model by
identifying the RFI in the actual observational data from LOFAR and MeerKAT.
The results show that the model performs well. The overall effectiveness is
comparable to AOFlagger software and provides an improvement over existing
methods in some instances.
