To accurately assess the health status of 35 kV switchgear and assist in the formulation of maintenance plans, this paper proposes an evaluation method that combines cloud model and evidence theory. By establishing an evaluation system and introducing the cloud model to address the ambiguity of evaluation grades, a forward cloud generator is employed to transform membership distributions into evidential forms, tackling data uncertainty. Furthermore, a comprehensive weighting method that integrates static and dynamic weights is used to adjust conflicting data, and the average fitting degree method is applied to determine the evaluation results. Case analysis using actual data demonstrates that, compared to traditional methods, the proposed method exhibits advantages in handling uncertainty and conflicting data. The evaluation results are consistent with the actual operational status, offering valuable references for the assessment and engineering maintenance of 35 kV switchgear.