With the development of cyberspace assets detection technology, more and morevulnerable assets are exposed to the public, which will increase the security risk of cyber assetsto a certain extent. Vulnerability assessment of network assets can help people discovervulnerable and high-risk assets in time, and proactively protect and repair vulnerable networkassets when security events do not occur, which can effectively reduce the probability of networksecurity events. The existing researches mainly focus on the vulnerability assessmentsof the network assets and the network system, but rarely on the vulnerability assessment methods. In order to better protect the security of network assets, a vulnerability assessmentmodel of network assets based on particle swarm optimization algorithm-light gradient boostingmachine (PSO-LightGBM) was proposed. First, according to the industry standards andexpert experiences, an evaluation index system for vulnerability of network assets was proposed.On the basis of the network asset data crawled from the network, a network asset vulnerabilityassessment data set with 12 attribute characteristics and 11 types of label valueswas constructed after pretreatment. Then, light gradient boosting machine(LightGBM)model was combined with particle swarm optimization (PSO) algorithm to realize automaticvulnerability assessment of network assets by machine learning method. Finally, the effectivenessof the network asset vulnerability assessment model based on PSO-LightGBM wasverified by comparing the performance of several machine learning models on the data set.The experimental results show that this model can accurately assess the vulnerability of networkassets, with an accuracy of 91.24%.