As the construction of the Internet of Things (IoT) for power distribution continues, millions of power distribution devices, electrical quantity sensors, and status sensors will be connected to the IoT network. This also gives rise to a huge number and variety of heterogeneous distribution data in the IoT, whose security and stability are of increasing concern. However, due to the complexity and dynamics of the computer network environment at the distribution edge, traditional risk assessment methods are often difficult to comprehensively and effectively identify and assess potential unknown risks. Therefore, this paper constructs a method for analyzing and assessing the unknown risks of computer networks at the distribution edge, aiming to guarantee the information security of the distribution system. Edge computing technology is introduced to redefine the relationship between cloud, management, and end, and an edge computing platform is deployed on the end side to achieve real-time and efficient lightweight data processing in situ, and to collaborate with a new generation of distribution automation cloud master to achieve distribution station autonomy in terms of network, data, and business. The method first constructs a multi-dimensional risk characterization indicator system by collecting multi-source data from the distribution edge computer network to comprehensively reflect the changing trends of the network state. Then, machine learning and data mining techniques are used to perform correlation analysis and pattern recognition of risk features to discover potential risk factors and their correlations. The communication transmission losses of the edge computing network are all below 1.5%.And the risk analysis accuracy of the algorithm is 88.08%, which is much higher than the network risk analysis accuracy of other methods.