  We propose a lightweight deep convolutional neural network (lCNN) to estimate
cosmological parameters from simulated three-dimensional DM halo distributions
and associated statistics. The training dataset comprises 2000 realizations of
a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over
a cubic grid of $300^3$ voxels, with each simulation produced using $512^3$ DM
particles and $512^3$ neutrinos . Under the flat $\Lambda$CDM model,
simulations vary standard six cosmological parameters including $\Omega_m$,
$\Omega_b$, $h$, $n_s$, $\sigma_8$, $w$, along with the neutrino mass sum,
$M_\nu$. We find that: 1) within the framework of lCNN, extracting large-scale
structure information is more efficient from the halo density field compared to
relying on the statistical quantities including the power spectrum, the
two-point correlation function, and the coefficients from wavelet scattering
transform; 2) combining the halo density field with its Fourier transformed
counterpart enhances predictions, while augmenting the training dataset with
measured statistics further improves performance; 3) achieving high accuracy in
inferring $\Omega_m$, $h$, $n_s$, and $\sigma_8$ by the neural network model,
while being inefficient in predicting $\Omega_b$,$M_\nu$ and $w$; 4) compared
to the simple random forest network trained with three statistical quantities,
lCNN yields unbiased estimations with reduced statistical errors: approximately
33.3\% for $\Omega_m$, 20.0\% for $h$, 8.3\% for $n_s$, and 40.0\% for
$\sigma_8$. Our study emphasizes this lCNN-based novel approach in extracting
large-scale structure information and estimating cosmological parameters.
