  Detecting gravitational waves from a nearby core-collapse supernova would
place meaningful constraints on the supernova engine and nuclear equation of
state. Here we use Convolutional Neural Network models to identify the core
rotational rates, rotation length scales, and the nuclear equation of state
(EoS), using the 1824 waveforms from Richers et al. (2017) for a 12 solar mass
progenitor. High prediction accuracy for the classifications of the rotation
length scales ($93\%$) and the rotational rates ($95\%$) can be achieved using
the gravitational wave signals from -10 ms to 6 ms core bounce. By including
additional 48 ms signals during the prompt convection phase, we could achieve
$96\%$ accuracy on the classification of four major EoS groups. Combining three
models above, we could correctly predict the core rotational rates, rotation
length scales, and the EoS at the same time with more than $85\%$ accuracy.
Finally, applying a transfer learning method for additional 74 waveforms from
FLASH simulations (Pan et al. 2018), we show that our model using Richers'
waveforms could successfully predict the rotational rates from Pan's waveforms
even for a continuous value with a mean absolute errors of 0.32 rad s$^{-1}$
only. These results demonstrate a much broader parameter regimes our model can
be applied for the identification of core-collapse supernova events through GW
signals.
