The grid integration of large-scale electric vehicles （EVs）with strong randomness has become a major bottleneck restricting the construction of new power systems.Their disordered charging behavior may lead to voltage violations and line overloads in distribution networks，seriously impacting system security and stability.To address this，this paper proposes a hosting capacity assessment method for distribution networks with EV charging load based on multi-boundary point approximation.By quantifying the rechargeable areas of a distribution network to accommodate EVs，it provides a theoretical basis for charging guidance strategies.First，considering the impact of spatiotemporal coupling characteristics among charging nodes on available charging power，a multi-period distribution network assessment model is constructed that incorporates the interactions of multiple charging stations.This model fully integrates distribution network operational constraints with the spatiotemporal distribution characteristics of EV charging loads.Second，based on the equivalent mapping theory of boundary point search，the feasible region of the distribution network under EV integration is characterized，effectively resolving the assessment deviations caused by traditional methods that ignore coupling effects.Furthermore，the impact of EV participation in demand response on distribution network hosting capacity is explored，revealing that demand response，as a key supplementary mechanism for adapting to large-scale EV development，can enhance system accommodation capacity for EVs by smoothing load curves.Finally，case studies based on a modified IEEE 33-node system show that the proposed method achieves a feasible region assessment error of only 1.77% during peak，flat，and valley periods，significantly outperforming traditional Monte Carlo sampling and multiple optimization methods .This verifies the effectiveness and accuracy of the proposed model and solution method，providing key technical support for ensuring high-quality power supply and secure operation of distribution networks.