  It is non-trivial to recognize phase transitions and track dynamics inside a
stochastic process because of its intrinsic stochasticity. In this paper, we
employ the deep learning method to classify the phase orders and predict the
damping coefficient of fluctuating systems under Langevin's description. As a
concrete set-up, we demonstrate this paradigm for the scalar condensation in
QCD matter near the critical point, in which the order parameter of chiral
phase transition can be characterized in a $1+1$-dimensional Langevin equation
for $\sigma$ field. In a supervised learning manner, the Convolutional Neural
Networks(CNNs) accurately classify the first-order phase transition and
crossover based on $\sigma$ field configurations with fluctuations. Noise in
the stochastic process does not significantly hinder the performance of the
well-trained neural network for phase order recognition. For mixed dynamics
with diverse dynamical parameters, we further devise and train the machine to
predict the damping coefficients $\eta$ in a broad range. The results show that
it is robust to extract the dynamics from the bumpy field configurations.
