The scope of this research lies in diagnosis of bladder cancer through Raman spectra. The spectra of bladder cancer and normal bladder were measured by using laser confocal Raman micro-spectroscopy. Principal component analysis/support vector machines was applied to the spectral dataset to construct diagnostic algorithms, then to detect the accuracy of these algorithms to determine histological diagnosis by leave-one-out cross validation from its Raman spectrum. It was showed that the peak intensity of nucleic acid (782, 1 583 cm-1) in bladder cancer and protein (1 061, 1 295, 2 849, 2 881 cm-1) in normal bladder increased significantly. Additionally, Principal component analysis (PCA) and support vector machines (SVM) provided an effective tool for differentiating the bladder cancer from normal bladder tissue. Excellent sensitivity (86.7%), specificity (87.5%), positive predictive value (92.9%), and negative predictive value (72.8%) for the diagnosis of bladder cancer were obtained by leave-one-out cross validation. It was concluded that Raman spectroscopy can be used to accurately identify bladder cancer in vitro, and it suggests the promising potential application of PCA/SVM-based Raman spectroscopy for the diagnosis of bladder cancer.