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Support vector machine (SVM) as the new machine learning method, with the advantages of solving the problem of small sample, high dimension and local extremum problems, with outstanding performance in the current fault diagnosis. According to the research on support vector machine and found that it has difficulty in parameter selection of classification model, this paper proposes an improved particle swarm optimization algorithm to solve the particle swarm pre convergence speed causes the latter easily optimization inequality.The particle swarm optimization algorithm combined with support vector machine classification model used for the detection and diagnosis of bearing fault case analysis method, the superiority and improvement of support vector machine in fault diagnosis process accuracy. Through the actual detection, the improved SVM method has a good diagnosis function for clustering fault classification.