For broadband communication signal detection problem, as the current signal detection algorithm based on deep learning is not suitable for dealing with large bandwidth and large wide broadband signals, and there is the inherent deviation in signal frequency parameter estimation, we put forward intelligent broadband communication signal detection algorithm based on spectrum decomposition, thus to complete highly accurate detection of narrow-band signal in large bandwidth receiving signal. First, the broadband signal is transformed into a grayscale time-frequency spectrum which is subsequently decomposed into a sub-spectrum suitable for the input size of the target detection network. Then, the anchor-free YOLOx target detection algorithm is used to detect the narrowband signal targets in the sub-spectrum. Finally, the signal detection results of the sub-spectrum are fused to obtain the detection results of the time-frequency parameters of the narrow-band signal. Experimental results show that the proposed algorithm can adapt to the complex noise environment. Compared with other deep learning algorithms and traditional energy detection algorithms, the proposed algorithm has higher signal detection accuracy, lower false alarm probability, smaller average error of signal parameter estimation, and stronger robustness, practicability and versatility.