  In the power spectra of oscillating red giants, there are visually distinct
features defining stars ascending the red giant branch from those that have
commenced helium core burning. We train a one-dimensional convolutional neural
network by supervised learning to automatically learn these visual features
from images of folded oscillation spectra. By training and testing on
\textit{Kepler} red giants, we achieve an accuracy of up to 99\% in separating
helium-burning red giants from those ascending the red giant branch. The
convolutional neural network additionally shows capability in accurately
predicting the evolutionary states of 5379 previously unclassified
\textit{Kepler} red giants, by which we now have greatly increased the number
of classified stars.
