  The wide application of machine learning (ML) techniques in statistics
physics has presented new avenues for research in this field. In this paper, we
introduce a semi-supervised learning method based on Siamese Neural Networks
(SNN), trying to explore the potential of neural network (NN) in the study of
critical behaviors beyond the approaches of supervised and unsupervised
learning. By focusing on the (1+1) dimensional bond directed percolation (DP)
model of nonequilibrium phase transition, we use the SNN to predict the
critical values and critical exponents of the system. Different from
traditional ML methods, the input of SNN is a set of configuration data pairs
and the output prediction is similarity, which prompts to find an anchor point
of data for pair comparison during the test. In our study, during test we set
different bond probability $p$ as anchors, and discuss the impact of the
configurations at this anchors on predictions. More, we use an iterative method
to find the optimal training interval to make the algorithm more efficient, and
the prediction results are comparable to other ML methods.
