With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network, and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network, algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example designs a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

随着计算机网络的广泛应用，网络安全受到越来越多的关注。网络上的各种攻击可能对网络安全构成极大威胁的主要原因是计算机网络系统本身的漏洞。将神经网络技术引入计算机网络脆弱性评估可以充分发挥神经网络在网络脆弱性评估中​​的优势。本文的目的是通过组织特征图神经网络，并结合多层前馈神经网络，将训练样本采用SOM神经网络聚类，将聚类结果添加到原始训练样本中并设置一定的权重，基于不断地进行加权迭代更新，以提高BP神经网络的收敛速度。