  When applying the foreground removal methods to uncover the faint
cosmological signal from the epoch of reionization (EoR), the foreground
spectra are assumed to be smooth. However, this assumption can be seriously
violated in practice since the unresolved or mis-subtracted foreground sources,
which are further complicated by the frequency-dependent beam effects of
interferometers, will generate significant fluctuations along the frequency
dimension. To address this issue, we propose a novel deep-learning-based method
that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate the
EoR signal. After being trained on the SKA images simulated with realistic beam
effects, the CDAE achieves excellent performance as the mean correlation
coefficient ($\bar{\rho}$) between the reconstructed and input EoR signals
reaches $0.929 \pm 0.045$. In comparison, the two representative traditional
methods, namely the polynomial fitting method and the continuous wavelet
transform method, both have difficulties in modelling and removing the
foreground emission complicated with the beam effects, yielding only
$\bar{\rho}_{\text{poly}} = 0.296 \pm 0.121$ and $\bar{\rho}_{\text{cwt}} =
0.198 \pm 0.160$, respectively. We conclude that, by hierarchically learning
sophisticated features through multiple convolutional layers, the CDAE is a
powerful tool that can be used to overcome the complicated beam effects and
accurately separate the EoR signal. Our results also exhibit the great
potential of deep-learning-based methods in future EoR experiments.
