  Data, algorithms, and arithmetic power are the three foundational conditions
for deep learning to be effective in the application domain. Data is the focus
for developing deep learning algorithms. In practical engineering applications,
some data are affected by the conditions under which more data cannot be
obtained or the cost of obtaining data is too high, resulting in smaller data
sets (generally several hundred to several thousand) and data sizes that are
far smaller than the size of large data sets (tens of thousands). The above two
methods are based on the original dataset to generate, in the case of
insufficient data volume of the original data may not reflect all the real
environment, such as the real environment of the light, silhouette and other
information, if the amount of data is not enough, it is difficult to use a
simple transformation or neural network generative model to generate the
required data. The research in this paper firstly analyses the key points of
the data enhancement technology of graph neural network, and at the same time
introduces the composition foundation of graph neural network in depth, on the
basis of which the data enhancement technology of graph neural network is
optimized and analysed.
