Traditional reliability models assume subsystem failures are independent, ignoring dependencies and leading to bias. To address this, a dependent reliability modeling method based on Copula and multi-index fusion is proposed. The rank-sum method integrates multi-dimensional metrics to determine optimal lifetime distributions for each subsystem. The maximum information coefficient quantifies fault dependency between subsystems and identifies dependent pairs. The Archimedean Copula models the dependence structure, parameters estimated via maximum likelihood, and optimal Copula selected using goodness-of-fit metrics. Empirical studies with real failure data show that, compared to independent models, the proposed method improves assessment accuracy throughout the period, confirming dependency modeling provides a positive effect. Based on actual data, a framework covering lifetime fitting, dependency identification, and optimal Copula selection is constructed, offering a practical tool for systems with fault dependencies and reference for maintenance and risk control.