  With the continuous development of computer technology and network
technology, the scale of the network continues to expand, the network space
tends to be complex, and the application of computers and networks has been
deeply into politics, the military, finance, electricity, and other important
fields. When security events do not occur, the vulnerability assessment of
these high-risk network assets can be actively carried out to prepare for rainy
days, to effectively reduce the loss caused by security events. Therefore, this
paper proposes a multi-classification prediction model of network asset
vulnerability based on quantum particle swarm algorithm-Lightweight Gradient
Elevator (QPSO-LightGBM). In this model, based on using the Synthetic minority
oversampling technique (SMOTE) to balance the data, quantum particle swarm
optimization (QPSO) was used for automatic parameter optimization, and LightGBM
was used for modeling. Realize multi-classification prediction of network asset
vulnerability. To verify the rationality of the model, the proposed model is
compared with the model constructed by other algorithms. The results show that
the proposed model is better in various predictive performance indexes.
