  We apply a machine learning technique for identifying the topological charge
of quantum gauge configurations in four-dimensional SU(3) Yang-Mills theory.
The topological charge density measured on the original and smoothed gauge
configurations with and without dimensional reduction is used as inputs for the
neural networks (NN) with and without convolutional layers. The gradient flow
is used for the smoothing of the gauge field. We find that the topological
charge determined at a large flow time can be predicted with high accuracy from
the data at small flow times by the trained NN; for example, the accuracy
exceeds $99\%$ with the data at $t/a^2\le0.3$. High robustness against the
change of simulation parameters is also confirmed with a fixed physical volume.
We find that the best performance is obtained when the spatial coordinates of
the topological charge density are fully integrated out in preprocessing, which
implies that our convolutional NN does not find characteristic structures in
multi-dimensional space relevant for the determination of the topological
charge.
