AbstractIn recent decades, artificial intelligence has demonstrated successful applications in computer vision and natural language processing due to its breakthrough performance. In this paper, we propose a deep convolutional neural network (DCNN)‐based method, characterized by acoustic features, for FM broadcast monitoring. The results show that the proposed DCNN model trained with the FBank feature extracted from FM audio files is capable of achieving nearly 100% accuracy in FM station identification tasks. Illegal broadcasting signals are then detected through a comparison with a radio frequency station database. Integrated with the trained DCNN model, an online FM broadcast monitoring system has been successfully implemented and deployed to help reduce human labor and automate the task of FM broadcast monitoring.