  Active target time projection chambers are important tools in low energy
radioactive ion beams or gamma rays related researches. In this work, we
present the application of machine learning methods to the analysis of data
obtained from an active target time projection chamber. Specifically, we
investigate the effectiveness of Visual Geometry Group (VGG) and the Residual
neural Network (ResNet) models for event classification and reconstruction in
decays from the excited $2^+_2$ state in $^{12}$C Hoyle rotation band. The
results show that machine learning methods are effective in identifying
$^{12}$C events from the background noise, with ResNet-34 achieving an
impressive precision of 0.99 on simulation data, and the best performing event
reconstruction model ResNet-18 providing an energy resolution of $\sigma_E<77$
keV and an angular reconstruction deviation of $\sigma_{\theta}<0.1$ rad. The
promising results suggest that the ResNet model trained on Monte Carlo samples
could be used for future classifying and predicting experimental data in active
target time projection chambers related experiments.
