  Precise continuum normalisation of merged \'{e}chelle spectra is a demanding
task necessary for various detailed spectroscopic analyses. Automatic methods
have limited effectiveness due to the variety of features present in the
spectra of stars. This complexity often leads to the necessity of manual
normalisation which is a time demanding task. The aim of this work is to
develop a fully automated normalisation tool that works with order-merged
spectra and offers flexible manual fine-tuning, if necessary. The core of the
proposed method uses the novel fully convolutional deep neural network (SUPP
Network) that was trained to predict a pseudo-continuum. The post-processing
step uses smoothing splines that gives access to regressed knots useful for
optional manual corrections. The active learning technique was applied to deal
with possible biases that may arise from training with synthetic spectra and to
extend the applicability of the proposed method to features absent in this kind
of spectra. The developed normalisation method was tested with high-resolution
spectra of stars having spectral types from O to G, and gave root mean squared
(RMS) error over the set of test stars equal $0.0128$ in the spectral range
from $3900\,\r{A}$ to $7000\,\r{A}$ and $0.0081$ in the range from
$4200\,\r{A}$ to $7000\,\r{A}$. Experiments with synthetic spectra give RMS of
the order of $0.0050$. The proposed method gives results comparable to careful
manual normalisation. Additionally, this approach is general and can be used in
other fields of astronomy where background modelling or trend removal is a part
of data processing. The algorithm is available online at https://git.io/JqJhf.
