[Objective] High voltage isolation switches operate in harsh outdoor environments and are susceptible to external forces, natural aging, high temperatures, humidity, and other factors, which can lead to a series of faults and affect the healthy and normal operation of the power grid. This paper proposes a fault diagnosis method for isolation switches based on the Newton-Raphson-based optimizer (NRBO) improved feature mode decomposition (FMD) and support vector machine (SVM). [Methods] Firstly, NRBO was used to optimize the three parameters of FMD, and the optimal parameter combination was obtained. The vibration signals of the isolation switch collected in the experiment were decomposed by the FMD based on NRBO optimization (NRFMD), and the optimal intrinsic mode components were obtained. Secondly, the refined composite multiscale fluctuation dispersion entropy (RCMFDE) was used to extract the intrinsic mode components and obtain a high-dimensional feature matrix. Finally, the kernel principal component analysis was used to reduce the dimension of the high-dimensional feature matrix, and the SVM based on NRBO optimization (NRSVM) model was applied to diagnose the fault of the isolation switch. [Results] The fault simulation experiments were carried out for a 220 kV isolation switch, and the vibration signals of the isolation switch under four working states were collected. The fault diagnosis method proposed in this paper was compared with other commonly used diagnosis methods. The results showed that under different mechanical fault conditions, this method could achieve a fault classification accuracy of 98.33% for isolation switches, demonstrating high recognition accuracy, outperforming other commonly used algorithms. [Conclusion] The NRFMD used in this paper can ignore the periodicity and impulse of mechanical signals, exhibiting good robustness. RCMFDE can better extract the features of mode components. In summary, the proposed NRFMD-RCMFDE-NRSVM algorithm has good applicability for fault diagnosis of isolation switches, providing new insights for future research on isolation switch faults.