To accurately identify abnormal conditions in gas insulated switchgear (GIS) equipment, a voiceprint recognition algorithm is proposed based on weighted Mel frequency cestrum coefficient-one class support vector machine (MFCC-OCSVM) and Bayesian optimized bidirectional gate recurrent unit (BiGRU). Firstly, weighted extractions of voiceprint data are performed using MFCC based on the F-statistic, highlighting important features and reducing the influence of noise. Subsequently, OCSVM is utilized to detect anomalies and remove anomalous values from the weighted features to improve data quality. To address the issue of sample imbalance, synthetic minority over-sampling technique (SMOTE) is employed to balance voiceprint samples. Finally, voiceprint recognition is carried out using a BiGRU model based on Bayesian optimization. Taking a certain GIS equipment as an example, sound samples from 20 different operating conditions are collected and compared with various classical classification models. The results demonstrate that the proposed algorithm achieves the highest average recognition accuracy of 92.8%, resulting in improvements of 30.1%, 14.7% and 11.5% compared to adaptive boosting, Naïve Bayes, and linear discriminate analysis, respectively. Ablation study further assesses and validates the practical effects and performance impacts of each process in the proposed algorithm. Research results provide an efficient technical approach for voiceprint recognition of anomalous conditions in GIS.