The increasing load density and the higher proportion of external power feed-in continuously raise the level of short-circuit currents in the receiving-end transmission network, affecting the safe operation of the receiving-end grid. To address this issue, this paper proposes an optimization method for the receiving-end grid with multi-DC feed-in based on complex network theory. We investigate and propose an improved community detection algorithm based on complex network theory to search for critical interconnection lines in the receiving-end grid, forming a pre-disconnection line set. Targeting the improvement of short-circuit current level, the ratio of multiple feed-in short-circuit, and the active loss of the grid, we establish a multi-objective optimization model for the receiving-end grid framework. To enhance the accuracy of the model, we consider the impact of uncertainty in renewable energy output in the grid operation mode, utilizing non-parametric kernel density estimation and Frank-Copula function to generate scenarios of wind and solar output and cluster 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 method is validated through case studies.