[Objective] To solve the over-smoothing problem of the traditional graph neural network, realize the weight adaptive allocation of different depths and different neighbors of the graph neural network, and improve the performance of academic paper classification.

[Methods] An improved graph neural network academic paper classification model based on multi-head attention mechanism and residual network structure is proposed. First, based on the multi-head attention mechanism, it learns a variety of related features between documents, and realizes the adaptive distribution of the weight of different neighbor nodes; then, based on the residual network structure, the output of each layer node of the model is aggregated, and the learning of adaptive aggregation radius is provided for the model. Finally, based on the improved graph neural network, the feature representation of each node in the paper citation graph is learned, and the feature is input into the multi-layer fully connected network to obtain the final classification result.

[Results] The experimental results on large-scale real datasets show that the accuracy of the model reaches 61%, which is 4% and 14% higher than that of the traditional GCN and Transformer models, respectively.

[Limitations] The classification accuracy of samples with a small proportion of categories and samples that are difficult to distinguish is not high.

[Conclusions] The improved graph neural network can effectively avoid the over-smoothing problem and realize the adaptive allocation of different weights.