  Recent years have witnessed a growing interest in using machine learning to
predict and identify phase transitions in various systems. Here we adopt
convolutional neural networks (CNNs) to study the phase transitions of Vicsek
model, solving the problem that traditional order parameters are insufficiently
able to do. Within the large-scale simulations, there are four phases, and we
confirm that all the phase transitions between two neighboring phases are
first-order. We have successfully classified the phase by using CNNs with a
high accuracy and identified the phase transition points, while traditional
approaches using various order parameters fail to obtain. These results
indicate that the great potential of machine learning approach in understanding
the complexities in collective behaviors, and in related complex systems in
general.
