When deep learning is used to deal with the computer vision tasks, under little number of new task data, the pre-trained model weight based on a very large data is trained as an initial weight to get better generalization ability. At this point, former explanations are based on the intuitive analysis and lack of reasonable mathematical methods. In this paper, deep neural network, which trains on internal layers with fixed structure，changed into internal transfer ability in deep neural network. The changes of learning process are formalized into a mathematical expression. Considering the influence of the data set on the training process, the information geometric analysis method is used to determine the metrics and connections over manifolds of different data sets, which can realize the embedding mapping between different data sets. At the same time, the change of parameter space is also put into a manifold space to explore its common influence on learning process. Finally, a mathematical explanation is provided for the internal transfer phenomenon. Meanwhile, after the analysis and experiments, the process of internal transfer is identified as a change which can make the network search for optimal search in a wider space. Therefore, the model can obtain a relative better solution in learning process.