  The current deep neural network algorithm still stays in the end-to-end
training supervision method like Image-Label pairs, which makes traditional
algorithm is difficult to explain the reason for the results, and the
prediction logic is difficult to understand and analyze. The current algorithm
does not use the existing human knowledge information, which makes the model
not in line with the human cognition model and makes the model not suitable for
human use. In order to solve the above problems, the present invention provides
a deep neural network training method based on the human knowledge, which uses
the human cognition model to construct the deep neural network training model,
and uses the existing human knowledge information to construct the deep neural
network training model. This paper proposes a multi-level hierarchical deep
learning algorithm, which is composed of multi-level hierarchical deep neural
network architecture and multi-level hierarchical deep learning framework. The
experimental results show that the proposed algorithm can effectively explain
the hidden information of the neural network. The goal of our study is to
improve the interpretability of deep neural networks (DNNs) by providing an
analysis of the impact of knowledge injection on the classification task. We
constructed a knowledge injection dataset with matching knowledge data and
image classification data. The knowledge injection dataset is the benchmark
dataset for the experiments in the paper. Our model expresses the improvement
in interpretability and classification task performance of hidden layers at
different scales.
