The increasing load density and the higher proportion of external power infeed continuously raise the level of short-circuit current in the receiving-end transmission network,thereby affecting the safe operation of the receiving-end grid.To address this issue,we propose an optimization approach for the receiving-end grid with multi-infeed DC based on complex network theory.We investigate complex network theory and introduce an improved community detection algorithm specifically designed to pinpoint critical interconnection lines within the receiving-end grid,forming a pre-disconnection line set.Targeting at the improvement of short-circuit current level,the multi-infeed short-circuit ratio,and the active loss of the grid,a multi-objective optimization model is established for the receiving-end grid framework.To enhance the model accuracy,the impact of uncertainty in renewable energy output in the grid operation modes is considered,then the non-parametric kernel density estimation and Frank-Copula function are utilized to generate scenarios of wind and solar output,which are then clustered to obtain a set of typical scenarios.The NSGA-Ⅱ algorithm is employed to solve the model,obtaining a Pareto-optimal solution set,from which the optimal decision solution satisfying N-1 verification is selected.Finally,the effectiveness of the proposed approach is validated through case studies.