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In deep learning-based acoustic echo cancellation methods, neural networks implicitly learn echo paths to cancel echoes. However, under low signal-to-echo ratio conditions, the substantial energy discrepancy between the microphone signal and the reference signal impedes the network's ability, resulting in poor performance. In this study, we propose a Signal Decoupling-based monaural Acoustic Echo Cancellation method called SDAEC. Specifically, we model the energy of the reference signal and the microphone signal to obtain an energy scaling factor. The reference signal is then multiplied by this energy scaling factor before being fed into the subsequent echo cancellation network. This approach reduces the difficulty of the subsequent echo cancellation step, thereby improving the overall cancellation performance. Experimental results demonstrate that the proposed method enhances the performance of multiple baseline models.