Particle swarm optimization (PSO) optimized support vector machine (SVM) based rectal perception function rebuilding method was proposed for rectal perception loss caused by anal incontinence. By analyzing human rectum characteristics, highamplitude propagated contractions (HAPC) in rectal contractions were used to indicate an urge to defecate. Rectal pressure feature was extracted using wavelet packet analysis, taking normalized of wavelet packet coefficients mean and energy as feature vector. Rectal perception prediction model was trained based on SVM whose parameters are optimized by PSO. Then the trained model was used to predict the urge to defecate. And the prediction accuracy of the optimized and nonoptimized SVM with different kernel functions was compared. Experimental results show that the proposed method is effective in rebuilding patients’ rectal perception function.