To solve the problems of low efficiency and difficult feature extraction in traditional fault diagnosis methods, this study proposes an optimized Fuzzy C-Means clustering algorithm for diagnosing and analyzing gas turbine engine faults. This algorithm mainly introduces subtraction clustering, penalty factors, and data weights on the basis of the original fuzzy C-means clustering algorithm, thereby improving the generalization ability of the algorithm model and the credibility of the results. The optimized fuzzy C-means clustering algorithm had the highest level of accuracy value, with a value of 95.67%, which was 11.79% higher than the average accuracy of other algorithms. Meanwhile the optimized Fuzzy C-Means clustering algorithm improved the accuracy values of KNN, BP, SVM, and fuzzy C-means clustering algorithms by 19.65%, 12.26%, 3.55%, and 11.70%. The training set accuracy of the optimized fuzzy C-means clustering algorithm under four engine states was at the highest level, with an average improvement of 15.5%, 25%, 24%, and 16% in accuracy. The optimized fuzzy C-means clustering algorithm achieved an accuracy of 90.39% in the test set, with an average improvement of 16.13% in accuracy. The membership classification results indicated that the optimized fuzzy C-means clustering algorithm had a membership degree of 1.