  We present an innovative approach to constraining the non-cold dark matter
model using a convolutional neural network (CNN). We perform a suite of
hydrodynamic simulations with varying dark matter particle masses and generate
mock 21cm radio intensity maps to trace the dark matter distribution. Our
proposed method complements the traditional power spectrum analysis. We compare
our CNN classification results with those from the power spectrum of the
differential brightness temperature map of 21cm radiation, and find that the
CNN outperforms the latter. Moreover, we investigate the impact of baryonic
physics on the dark matter model constraint, including star formation,
self-shielding of HI gas, and UV background model. We find that these effects
may introduce some contamination in the dark matter constraint, but they are
insignificant when compared to the realistic system noise of the SKA
instruments.
