With the increasing scale of long-distance and large-capacity HVDC transmission, the problem of transient voltage stability of DC receiving-end system is becoming more and more serious. The rapid development of measurement technology and artificial intelligence provides a new idea for solving the transient voltage assessment problem of DC receiving-end system. Therefore, this paper proposes a multi-binary table method based on intelligent enhancement to evaluate the transient voltage of DC system. Firstly, the feature dimension is reduced by extracting the transient time series features of the DC system and using the ReliefF method. Then, the back propagation （BP） neural network is used to perform regression fitting on the transient voltage stability margin index, and the improved particle swarm optimization （PSO） algorithm is used to solve the multi-binary table integral weights. Finally, a voltage collapse model is built and verified by simulation. The results show that the proposed intelligent enhanced multi-binary table transient voltage assessment method has good accuracy and adaptability to the DC receiving-end system, and has the potential for online application.