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Detecting gravitational waves from a nearby core-collapse supernova wouldplace meaningful constraints on the supernova engine and nuclear equation ofstate. Here we use Convolutional Neural Network models to identify the corerotational rates, rotation length scales, and the nuclear equation of state(EoS), using the 1824 waveforms from Richers et al. (2017) for a 12 solar massprogenitor. High prediction accuracy for the classifications of the rotationlength scales ($93\%$) and the rotational rates ($95\%$) can be achieved usingthe gravitational wave signals from -10 ms to 6 ms core bounce. By includingadditional 48 ms signals during the prompt convection phase, we could achieve$96\%$ accuracy on the classification of four major EoS groups. Combining threemodels above, we could correctly predict the core rotational rates, rotationlength scales, and the EoS at the same time with more than $85\%$ accuracy.Finally, applying a transfer learning method for additional 74 waveforms fromFLASH simulations (Pan et al. 2018), we show that our model using Richers'waveforms could successfully predict the rotational rates from Pan's waveformseven 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 canbe applied for the identification of core-collapse supernova events through GWsignals.

Determine the Core Structure and Nuclear Equation of State of Rotating  Core-Collapse Supernovae with Gravitational Waves by Convolutional Neural  Networks